Search is not available for this dataset
query stringlengths 1 13.4k | pos stringlengths 1 61k | neg stringlengths 1 63.9k | query_lang stringclasses 147
values | __index_level_0__ int64 0 3.11M |
|---|---|---|---|---|
If Kerckhoffs's principle is correct why does the NSA not publish their Suite A ciphers? The offers little insight. To quote the page: the decision to keep them secret is in keeping with a layered security posture How should we (the cryptographic community) interpret the NSA's decision to keep their best ciphe... | Why are cryptography algorithms not exported to certain countries? There have been strict laws about the export of crypto software to certain countries. I can understand the intent, but never gauged the fact that there is nothing stopping these countries from developing their own crypto software. So why limit the expor... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,100 |
compute maximum number of black rectangles in a cutome given black and white board ATTENTION: this is not maximal rectangle problem. this is about number of rectangles. look at this picture: I am looking for an algorithm to compute total number of blue rectangles(not maximal rectangle). this algorithm must be O(n^2)... | Puzzle: Find largest rectangle (maximal rectangle problem) What's the most efficient algorithm to find the rectangle with the largest area which will fit in the empty space? Let's say the screen looks like this ('#' represents filled area): .................... ..............###### ##.................. ................. | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,101 |
Inverse eigenvalue of a linear transformation T is a linear transformation, and $\lambda$ is N eigenvalue of T. How do I prove that $\lambda^{-1}$ is an eigenvalue for $T^{-1}$? I know for a matrix, I can use the fact that $Av=\lambda v$, but how does a linear transformation work? | If A is invertible, prove that $\lambda \neq 0$, and $\vec{v}$ is also an eigenvector for $A^{-1}$, what is the corresponding eigenvalue? If A is invertible, prove that $\lambda \neq 0$, and $\vec{v}$ is also an eigenvector for $A^{-1}$, what is the corresponding eigenvalue? I don't really know where to start with thi... | Question on how to normalize regression coefficient Not sure if normalize is the correct word to use here, but I will try my best to illustrate what I am trying to ask. The estimator used here is least squares. Suppose you have $y=\beta_0+\beta_1x_1$, you can center it around the mean by $y=\beta_0'+\beta_1x_1'$ where... | eng_Latn | 1,102 |
Finding area of parallelogram If these are the vertices for a parallelogram, (0,0), (-1,3), (4,-5), (3,-2) how do I find the area? I know it's supposed to be a 2x2 matrix and I need to find the determinant for it but I don't know which columns of it to use. Am I supposed to draw it out? Am I supposed to shift one ... | Area of a parallelogram, vertices $(-1,-1), (4,1), (5,3), (10,5)$. I need to find the area of a parallelogram with vertices $(-1,-1), (4,1), (5,3), (10,5)$. If I denote $A=(-1,-1)$, $B=(4,1)$, $C=(5,3)$, $D=(10,5)$, then I see that $\overrightarrow{AB}=(5,2)=\overrightarrow{CD}$. Similarly $\overrightarrow{AC}=\overri... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,103 |
Python list comprehension with lambdas I'm running Python 3.4.2, and I'm confused at the behavior of my code. I'm trying to create a list of callable polynomial functions with increasing degree: bases = [lambda x: x**i for i in range(3)] But for some reason it does this: print([b(5) for b in bases]) # [25, 25, 25] ... | How do I create a list of Python lambdas (in a list comprehension/for loop)? I want to create a list of lambda objects from a list of constants in Python; for instance: listOfNumbers = [1,2,3,4,5] square = lambda x: x * x listOfLambdas = [lambda: square(i) for i in listOfNumbers] This will create a list of lambda ob... | Local polynomial regression: Why does the variance increase monotonically in the degree? How can I show that the variance of local polynomial regression is increasing with the degree of the polynomial (Exercise 6.3 in Elements of Statistical Learning, second edition)? This question has been asked but the answer just ... | eng_Latn | 1,104 |
How does AUC of ROC equal concordance probability? If is correct then I think I understand what concordance probability is. However, I also find that provides the formula of concordance probability that is a little bit different from the one on Quora: It include the counts of tied pairs with weight $ 0.5 $. Which one... | Why is ROC AUC equivalent to the probability that two randomly-selected samples are correctly ranked? I found there are two ways to understand what AUC stands for but I couldn't get why these two interpretations are equivalent mathematically. In the first interpretation, AUC is the area under the ROC curve. Picking p... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,105 |
Parametric vs non-parametric machine learning methods I looked-up many references and websites and researched on how to determine if a method is between parametric or non-parametric. I came up with below definitions, A parametric algorithm has a fixed number of parameters. In contrast, a non-parametric algorithm us... | What exactly is the difference between a parametric and non-parametric model? I am confused with the definition of non-parametric model after reading this link and . Originally I thought "parametric vs non-parametric" means if we have distribution assumptions on the model (similar to parametric or non-parametric hyp... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,106 |
Creating a list of matrices I am new to programming and I was wondering if my question has a simple implementation. I have a bunch of matrices and I want a way to be able to store them, or be able to easily call them and do operations on them. For example, if I have 100 matrices, called, M1,M2,...M100; is there a way I... | Array of Matrices in MATLAB I am looking for a way to store a large variable number of matrixes in an array in MATLAB. Are there any ways to achieve this? Example: for i: 1:unknown myArray(i) = zeros(500,800); end Where unknown is the varied length of the array, I can revise with additional info if needed. Upda... | Superscript outside math mode What is the easiest way to superscript text outside of math mode? For example, let's say I want to write the $n^{th}$ element, but without the math mode's automatic italicization of the th. And what if I still want the n to be in math mode, but the th outside? | eng_Latn | 1,107 |
How to determine number of pixels required to interface any vga display I have a guide that tells me that for interfacing 640x480 screen you need 800 pixels in a row and 521 lines along with all of that front and back porch stuff I wanna know how do they determine that number? If i have a screen of different resolution... | Programming pattern to generate VGA signal with micro-controller? I want to generate VGA signal with micro-controller (like TI Tiva ARM which runs at 90/120Mhz speed). I'm not sure how to make accurate timings with micro-controller. Which programming pattern I need to use? Do I need any inline assembler code? How to ... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,108 |
What is the right projection for area calculation in Kenya? I want to calculate the area of counties (1st admin level) in Kenya in ArcGIS. What is the right projection for that? I know I should use an equal area projection, but when I use them (I tried six different) it varies heavily to the county areas given in wikip... | Coordinate system for accurately calculating areas of polygons that cross UTM Zones? I have multiple polygons, all located offshore around the UK. I am trying to calculate the areas of these polygons in square kilometres, without splitting them by UTM zone. Is it possible to do this accurately? I have been using World... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,109 |
The expected value of days a miner will be stuck A miner is stranded and there are three paths that he can take. Path A loops back to itself and takes him 1 day to walk it. Path B loops back to itself and takes him 2 days to walk it. Path C is the exit and it takes him 3 days to walk it. Each path has an equal prob... | Probability brain teaser with infinite loop I found this problem and I've been stuck on how to solve it. A miner is trapped in a mine containing 3 doors. The first door leads to a tunnel that will take him to safety after 3 hours of travel. The second door leads to a tunnel that will return him to the mine after 5 ... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,110 |
Prove that is $A$ is skew-symmetric, then $X^TAX = 0$ for all $X = [x_1 x_2 \cdots x_n]^T$ Recall that a matrix $A$ is skew-symmetric if and only if $A^T = -A$. Prove that if $A$ is skew-symmetric, then $X^TAX = 0$ for all $X = [x_1 x_2 \cdots x_n]^T$ | $x^TAx=0$ for all $x$ when $A$ is a skew symmetric matrix Let $A$ be an $n\times n$ skew symmetric matrix. Show that $x^TAx =0 \ \forall x \in \mathbb R^n$. How to prove this? | Why are symmetric positive definite (SPD) matrices so important? I know the definition of symmetric positive definite (SPD) matrix, but want to understand more. Why are they so important, intuitively? Here is what I know. What else? For a given data, Co-variance matrix is SPD. Co-variance matrix is a important met... | eng_Latn | 1,111 |
Derivative with respect to the variance of Multivariate Gaussian I am working on a multivariate Gaussian distribution with mean $\mu \in \mathbb{R}^d$ and the variance-covariance $\Sigma \in S^{d\times d}$ where $S$ denotes the set of symmetric matrices. When I am trying to maximize the log-likelihood of some observat... | Prove that $\nabla_{\mathrm X} \mbox{tr} (\mathrm A \mathrm X^{-1} \mathrm B) = - \mathrm X^{-\top} \mathrm A^\top \mathrm B^\top \mathrm X^{-\top}$ Prove that $$\nabla_{\mathrm X} \mbox{tr} (\mathrm A \mathrm X^{-1} \mathrm B) = - \mathrm X^{-\top} \mathrm A^\top \mathrm B^\top \mathrm X^{-\top}$$ My proof is below.... | Extreme learning machine: what's it all about? I've been thinking about, implementing and using the Extreme Learning Machine (ELM) paradigm for more than a year now, and the longer I do, the more I doubt that it is really a good thing. My opinion, however, seems to be in contrast with scientific community where -- when... | eng_Latn | 1,112 |
Forecasting daily online visits in r We have three years of data for online visits at a daily level. We want to forecast the daily visits for the next 90 days. What would be the best method to capture weekday seasonality , holiday seasons, and also the drift. Can this be successfully done in R? We are currently using ... | Daily forecasting We have three years of data for online visits at a daily level. We want to forecast the daily visits for the next 90 days. What would be the best method to capture weekday seasonality , holiday seasons, and also the drift. Can this be successfully done in R? We are currently using R. We have consider... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,113 |
Recursive time series forecasting In a recursive forecasting model, let's say you are trying to predict sales for the next month and you will append that prediction to your input and predict the month after. Basically, your target is Sales quantity, which you lagged. But let's say you have other continuous numerical fe... | Recursive time series forecasting model In a recursive forecasting model, let's say you are trying to predict sales of Target for the next month and you will append that prediction to your input and predict the month after. Basically, your target is Sales quantity, which you lagged. But let's say you have other continu... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,114 |
One Hot Encode and Logistic Regression When using Logistic Regression, and the categorical variables are one hot encoded, do we always have to drop a variable to avoid the dummy variable trap? If I recall it correctly, I have seen somewhere that if you use regularization, you don't need to drop 1 variable, but I can't ... | Dropping one of the columns when using one-hot encoding My understanding is that in machine learning it can be a problem if your dataset has highly correlated features, as they effectively encode the same information. Recently someone pointed out that when you do one-hot encoding on a categorical variable you end up w... | L1 & L2 Regularization in Light GBM This question pertains to L1 & L2 regularization parameters in Light GBM. As per official documentation: reg_alpha (float, optional (default=0.)) – L1 regularization term on weights. reg_lambda (float, optional (default=0.)) – L2 regularization term on weights I have seen data... | eng_Latn | 1,115 |
I am trying to understand paper. It uses PCA and further uses LDA for dimensionality reduction. I have read about eigenfaces and PCA approach but I am not familiar with LDA and I am unable to understand the mathematics behind the scatter matrices : why to minimize the determinant of projected scatter matrices and ho... | Apparently, the Fisher analysis aims at simultaneously maximising the between-class separation, while minimising the within-class dispersion. A useful measure of the discrimination power of a variable is hence given by the diagonal quantity: $B_{ii}/W_{ii}$. I understand that the size (p x p) of the Betwe... | Assume I have a dataset for a supervised statistical classification task, e.g., via a Bayes' classifier. This dataset consists of 20 features and I want to boil it down to 2 features via dimensionality reduction techniques such as Principal Component Analysis (PCA) and/or Linear Discriminant Analysis (LDA). Both techn... | eng_Latn | 1,116 |
I ran principal components analysis in R on my data. All my regressors are continuous, non categorical variables, except gender which I excluded. I will add it and compare model 1 = PCA to model 2 = PCA + gender and see if its significant. I determined that PC1/2/3 determine 95% of the variance so I only consider the... | I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out noninformative input variables? Basically, I want to be able to order the original features in the data by variance or amount of i... | The entire site is blank right now. The header and footer are shown, but no questions. | eng_Latn | 1,117 |
I try to get the basic understanding behing SVM algorithm, however I have a problem with basic mathematics. I follow the lecture . Suppose the two classes can be separated by a hyperplane:$(w \cdot x) + b = 0$ Acoording to , hyperplane is defined as $n(r-r_0)=0$, does it mean that $b=-w \cdot r_0$? I tried to c... | How does a work, and what differentiates it from other linear classifiers, such as the , , or ? * (* I'm thinking in terms of the underlying motivations for the algorithm, optimisation strategies, generalisation capabilities, and run-time complexity) | Given $a + b = 1$, Prove that $a^ab^b + a^bb^a \le 1$; $a$ and $b$ are positive real numbers. | eng_Latn | 1,118 |
When working with a general matrix $A=$ \begin{bmatrix}a & b\\c & d\end{bmatrix}, I find that the eigenvalues are: $\lambda = \frac{d+a \pm \sqrt{(d-a)^2+4bc}}{2}$ I then find that eigenvectors, $e=$ \begin{bmatrix}-b\\a-\lambda\end{bmatrix}. I also know that under iteration with initial vector $x_n$ = \beg... | Suppose I am given a $2$x$2$ matrix $A=$ \begin{pmatrix} a & b \\ c & d \end{pmatrix} And an initial vector $x_n$ = \begin{pmatrix} x_0 \\ y_0\end{pmatrix}. Under repeated iteration $x_{n+1} = Ax_n$, we find that $x_{n+1}$ converges to a line in the direction of the $eigenvector$ of $A$ of the form $X$= $[u,... | Suppose I am given a $2$x$2$ matrix $A=$ \begin{pmatrix} a & b \\ c & d \end{pmatrix} And an initial vector $x_n$ = \begin{pmatrix} x_0 \\ y_0\end{pmatrix}. Under repeated iteration $x_{n+1} = Ax_n$, we find that $x_{n+1}$ converges to a line in the direction of the $eigenvector$ of $A$ of the form $X$= $[u,... | eng_Latn | 1,119 |
I know that Linear Discrimination Analysis (LDA) is used for classification and Multiple Linear Regression (MLR) is for regression. Lets say I have a matrix X (independent variables) and Y(dependent variables) if the Y is continues and I make a regression , it becomes MLR if the Y is categories (like 1111122222) and I... | Is there a relationship between regression and linear discriminant analysis (LDA)? What are their similarities and differences? Does it make any difference if there are two classes or more than two classes? | I am working with dimensionality reduction algorithms. Linear Discriminant Analysis (LDA) is a supervised algorithm that takes into account the class label (which is not the case of PCA for example). I am using Python to do a comparative study between some algorithms. Why with two classes (k = 2), regardless of the da... | eng_Latn | 1,120 |
What are the mathematical steps to get loadings and scores matrices of a 3x3 matrix basing of PCA and what is the relationship relating eigenvalues eigenvectors with loadings and score? | In today's pattern recognition class my professor talked about PCA, eigenvectors and eigenvalues. I understood the mathematics of it. If I'm asked to find eigenvalues etc. I'll do it correctly like a machine. But I didn't understand it. I didn't get the purpose of it. I didn't get the feel of it. I strongly be... | The score for the 4 questions are 111, 1218, 261 and 5 respectively, but they display 2 digits per line. This is an obvious bug in design. | eng_Latn | 1,121 |
I have too many environmental variables to use in a multiple regression analysis. If I use all the variables the models are just too complex. The use of the PCA axes in the regression analysis was impossible to interpret (since there wasn't a clear correlation with environmental variables), so we chose to select a limi... | I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out noninformative input variables? Basically, I want to be able to order the original features in the data by variance or amount of i... | For a given data matrix $A$ (with variables in columns and data points in rows), it seems like $A^TA$ plays an important role in statistics. For example, it is an important part of the analytical solution of ordinary least squares. Or, for PCA, its eigenvectors are the principal components of the data. I understand ho... | eng_Latn | 1,122 |
I am doing a multiple linear regression, in order to see whether the predictors are positively (positive $\beta$s) or negatively correlated (negative $\beta$s) with the response. Since I have a lot of predictors, I use Lasso to select variables. With cross-validation, I choose my optimal $\lambda$, but I am left with ... | I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise regression and why not to use it. I've been reading into LASSO regression and its ability for feature selection and ha... | Take a sponge ball and compress it. The net force acting on the body is zero and the body isn't displaced. So can we conclude that there is no work done on the ball? | eng_Latn | 1,123 |
Extracting sub-matrices from a matrix | How to divide an image into blocks in MATLAB? | Subspace of Noetherian space still Noetherian | eng_Latn | 1,124 |
LaTex problem with matrix left alignment on the margin | Align equation left | Every partial order can be extended to a linear ordering | eng_Latn | 1,125 |
l1 and l2 regularizations | Why L1 norm for sparse models | When will L1 regularization work better than L2 and vice versa? | eng_Latn | 1,126 |
Matrix library for large matrices? | What are the most widely used C++ vector/matrix math/linear algebra libraries, and their cost and benefit tradeoffs? | A fiber bundle over Euclidean space is trivial. | eng_Latn | 1,127 |
What is pca.components_ in sk-learn? I've been reading some documentation about PCA and trying to use scikit-learn to implement it. But I struggle to understand what are the attributes returned by sklearn.decompositon.PCA From what I read and the name of this attribute my first guess would be that the attribute .compo... | Relationship between SVD and PCA. How to use SVD to perform PCA? Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix $\mathbf X$. How does it work? What is the connection ... | Multicollinearity when individual regressions are significant, but VIFs are low I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. However, when I ran a linear regression compa... | eng_Latn | 1,128 |
What is the gradient of $x^T A\, x$ with respect to the matrix $A$? I have seen many times that the gradient of $x^TA\,x$ with respect to $x$ is $2A\,x$. But how do you find its gradient with respect to $A$? | Gradient of $a^T X b$ with respect to $X$ How can I find the gradient of the term $a^TXb$ where $X$ is a $n \times m$ matrix, and $a$ and $b$ are column vectors. Since the gradient is with respect to a matrix, it should be a matrix. But I do not have a clue on how to derive this gradient. Any help ? | How does Natural Selection shape Genetic Variation? Background Importance of the additive genetic variance As stated , the fundamental theorem of Natural Selection (NS) by Fisher says: The rate of increase in the mean fitness of any organism at any time ascribable to NS acting through changes in gene frequencies ... | eng_Latn | 1,129 |
Framework for Genetic Algorithms on python I'm trying to use a framework implemented in python to use GA (Genetic algorithms) and other related algorithms . But I'm not sure about what framework to use, I've found two interesting options Pymoo and JmetalPy, the former it seems well documented it and the latter isn't lo... | Python metaheuristic packages I need to use a metaheuristic algorithm to solve an optimization problem on a Python codebase. Metaheuristics usually need to be written in C++ or Java as they involve a lot of iterations, while Python is weak from this point of view. Questions: do any Python metaheuristic packages whi... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,130 |
Maximum likelihood estimator compared to least squares The maximum likelihood estimator is often compared to the least squares method. I'm struggling to see how these things are related at all. I would like to understand what the maximum likelihood estimator means in practice. Can you adapt the maximum likelihood estim... | Equivalence between least squares and MLE in Gaussian model I am new to Machine Learning, and am trying to learn it on my own. Recently I was reading through and had a basic question. Slide 13 says that "Least Square Estimate is same as Maximum Likelihood Estimate under a Gaussian model". It seems like it is somethi... | Representative point of a cluster with L1 distance The representative point of a cluster or cluster center for the k-means algorithm is the component-wise mean of the points in its cluster. The mean is chosen because it helps to minimize the within cluster variances (which is to say that it is minimizing within cluster... | eng_Latn | 1,131 |
Is standardization required by PCA? I have two questions related to the PCA: Is the higher weight assigned to the variable which not necessarily have higher variation but are more representative of the variation of other variables? (i.e. the PCA aims to catch the common variation) Since the input of PCA is just the c... | PCA on correlation or covariance? What are the main differences between performing principal component analysis (PCA) on the correlation matrix and on the covariance matrix? Do they give the same results? | Theory behind partial least squares regression Can anyone recommend a good exposition of the theory behind partial least squares regression (available online) for someone who understands SVD and PCA? I have looked at many sources online and have not found anything that had the right combination of rigor and accessibil... | eng_Latn | 1,132 |
Do we always assume cross entropy cost function for logistic regression solution unless stated otherwise? I am using Matlab glmfit for logistic regression. Now I know that usually people use the cross entropy to evaluate the error in predictions against the true labels ( which different than the linear regression where... | Which loss function is correct for logistic regression? I read about two versions of the loss function for logistic regression, which of them is correct and why? From (in Chinese), with $\beta = (w, b)\text{ and }\beta^Tx=w^Tx +b$: $$l(\beta) = \sum\limits_{i=1}^{m}\Big(-y_i\beta^Tx_i+\ln(1+e^{\beta^Tx_i})\Big) \tag... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,133 |
Discovering interactions and transformations I am teaching myself regression using Regression Modeling Strategies by Harell and the author goes at quite the length to showcase the importance of modeling interactions and transformations of the initial variables. I can't help but wonder how to approach this in a more str... | Discovering transformations and interactions I am teaching myself regression using Regression Modeling Strategies by Harell and the author goes at quite the length to showcase the importance of modeling interactions and transformations of the initial variables. I can't help but wonder how to approach this in a more str... | How to reverse PCA and reconstruct original variables from several principal components? Principal component analysis (PCA) can be used for dimensionality reduction. After such dimensionality reduction is performed, how can one approximately reconstruct the original variables/features from a small number of principal c... | eng_Latn | 1,134 |
78L05 questions What are the capacitors on the input and output of a 78L05 for? I saw a lot of different schematics with a lot of different input and output capacitor values. So now I am really confused, and I'm just wondering what is their purpose :D This is the one I made at first, but as I got deeper into it, I ... | What's the purpose of two capacitors in parallel? What's the purpose of the two capacitors in parallel on each side of the regulator in I've seen similar setups in other similar circuits and can guess that it's related to one being polarized an one not, but I don't really understand what's going on there. | Principal component analysis "backwards": how much variance of the data is explained by a given linear combination of the variables? I have carried out a principal components analysis of six variables $A$, $B$, $C$, $D$, $E$ and $F$. If I understand correctly, unrotated PC1 tells me what linear combination of these var... | eng_Latn | 1,135 |
How do we know from representation theory that a massless spin-1 particle has only two polarizations? In chapter 8.2.3 of Schwartz' textbook "Quantum Field Theory and the Standard Model", the author states the following, Finally, we expect from representation theory that there should only be two polarizations for ... | Counting degrees of freedom of gauge bosons Gauge bosons are represented by $A_{\mu}$, where $\mu = 0,1,2,3$. So in general there are 4 degrees of freedom. But in reality, a photon (gauge boson) has two degrees of freedom (two polarization states). So, when someone asks about on-shell and off-shell degrees of freedom... | How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)? Suppose I am running a regression $Y \sim X$. Why by selecting top $k$ principle components of $X$, does the model retain its predictive power on $Y$? I understand that from dimensionality-reduct... | eng_Latn | 1,136 |
Regarding the ellipsoid computed by using the “lifting step” I have asked a question at mathstackexchange but i have not got an answer yet so i am referencing the question here so that maybe one of you can help in understanding the problem: Please do help and thanks in advance. (and sorry for the inconvience) | Regarding the ellipsoid computed by using the "lifting step" I need your expertise in understanding and hopefully solving the following problem: Let $C \subset \mathbb{R}^d$, and lets assume that $C$ is not centrally symmetric, thus when computing the $MVEE(C)$ (i.e the minimum volume enclosing ellipsoid), they use th... | How do comments work? Across the Stack Exchange network you may leave comments on a question or answer. How do comments work? What are comments for, and when shouldn't I comment? Who can post comments? Who can edit comments? How can I format and link in comments? My comment doesn't contain some of the text I typed in ... | eng_Latn | 1,137 |
Do I need to perform variable selection before running a ridge regression? I am currently constructing a model that uses last year's departmental information to predict employee churn for the current year. I have 55 features and 318 departments in my data set. A good portion of my independent variables are correlated... | Before running a ridge regression model, do I need to preform variable selection? I am currently constructing a model that uses last year's departmental information to predict employee churn for the current year. I have 55 features and 318 departments in my data set. A good portion of my independent variables are cor... | Including Interaction Terms in Random Forest Suppose we have a response Y and predictors X1,....,Xn. If we were to try to fit Y via a linear model of X1,....,Xn, and it just so happened that the true relationship between Y and X1,...,Xn wasn't linear, we might be able to fix the model by transforming the X's somehow an... | eng_Latn | 1,138 |
How do you test multi-step time series forecasting? Suppose you have n observations of a time series dataset. You split it up to n-k (train data) and k (test data) observations. You train a model using the train data and you can now make predictions. From what I understand multi-step forecasting means you want to predi... | Using k-fold cross-validation for time-series model selection Question: I want to be sure of something, is the use of k-fold cross-validation with time series is straightforward, or does one need to pay special attention before using it? Background: I'm modeling a time series of 6 year (with semi-markov chain), with ... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,139 |
Is a $1\times 1 $ matrix a scalar? Intuitively I used to think that a $1\times 1$ matrix is simply a scalar number, I also saw this statement in books. However when I think about it now it doesn't make sense to me because of one problem. Let $I$ be the $2\times 2 $ identity matrix, now $\lambda I$ is well defined if ... | is a one-by-one-matrix just a number (scalar)? I was wondering. Clearly, we cannot multiply a (1x1)-matrix with a (4x3)-matrix; However, we can multiply a scalar with a matrix. This suggests a difference. On the other hand, I was, for example, in an econometrics lecture today, where we had for a (Tx1)-vector $\underli... | $\sum_i x_i^n = 0$ for all $n$ implies $x_i = 0$ Here is a statement that seems prima facie obvious, but when I try to prove it, I am lost. Let $x_1 , x_2, \dots, x_k$ be complex numbers satisfying: $$x_1 + x_2+ \dots + x_k = 0$$ $$x_1^2 + x_2^2+ \dots + x_k^2 = 0$$ $$x_1^3 + x_2^3+ \dots + x_k^3 = 0$$ $$\dots$$ ... | eng_Latn | 1,140 |
When are is Bayes estimator the same as the maximum likelihood estimator? Suppose we have the a random variable $X$, which is normally distributed $N(\theta,1)$ and we estimate $\theta$ using the squared error loss function, $L = (\theta, d) = (\theta - d)^2$. To find the Bayes estimator, we find $d$ which minimizes th... | What is the difference in Bayesian estimate and maximum likelihood estimate? Please explain to me the difference in Bayesian estimate and Maximum likelihood estimate? | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,141 |
Does variable separable method give the complete set of solutions to a PDE? Several of the common partial differential equations encountered in physics are mentioned as easy to solve using the variable separable method. However I do not understand how does one guarantee that the solutions generated using this method is... | Does separation of variables in PDEs give a general solution? When a partial differential equation is solved using the separation of variables method, is the produced solution the most general one that satisfies the equation or have we lost some forms of the solution because of the assumption that it is in the form of ... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,142 |
What is would be the word for deriving from experience or past performance While empirical means deriving from experiment and observation rather than theory. What is would be the word for deriving from experience or past performance. | What's a word for knowing something from experience? I'm trying to figure out a word that describes subconsciously knowing something from experience. My initial attempt was "instinct", but that has more gifted and primal connotations. The use case I had in mind was as follows: "If we assume that the model is appli... | Bottom to top explanation of the Mahalanobis distance? I'm studying pattern recognition and statistics and almost every book I open on the subject I bump into the concept of Mahalanobis distance. The books give sort of intuitive explanations, but still not good enough ones for me to actually really understand what is g... | eng_Latn | 1,143 |
How does the solution for polynomial regression depend on the number of data points and the error distribution Let the set of instances be generated by the function , where $ε$ is random, uniformly distributed noise. Also, assume that you are using a fifth degree polynomial for regression. | derivative of cost function for Logistic Regression I am going over the lectures on Machine Learning at Coursera. I am struggling with the following. How can the partial derivative of $$J(\theta)=-\frac{1}{m}\sum_{i=1}^{m}y^{i}\log(h_\theta(x^{i}))+(1-y^{i})\log(1-h_\theta(x^{i}))$$ where $h_{\theta}(x)$ is define... | Before running a ridge regression model, do I need to preform variable selection? I am currently constructing a model that uses last year's departmental information to predict employee churn for the current year. I have 55 features and 318 departments in my data set. A good portion of my independent variables are cor... | eng_Latn | 1,144 |
Sampling from multivariate normal using a pseudo-inverse Say I have an improper multivariate normal distribution with singular precision matrix $Q$. I can compute the pseudo-inverse of $Q$ and use this as a covariance matrix, i.e. $\Sigma = Q ^{+}$. I seem to be able to sample from this distribution using the standard ... | Generating samples from singular Gaussian distribution Let random vector $x = (x_1,...,x_n)$ follow multivariate normal distribution with mean $m$ and covariance matrix $S$. If $S$ is symmetric and positive definite (which is the usual case) then one can generate random samples from $x$ by first sampling indepently $r_... | How do I appropriately examine the dimensionality of binary data? I have 72 binary variables and, at a theoretical level, I am trying to identify groups of variables that seem to vary together. In practice, I am struggling with how to analyze this data properly. I am using R and the psych package. Here is what I have t... | eng_Latn | 1,145 |
can i use random forest for feature selection and then use poisson regression for model fitting? Variables that are important in random forest don't necessarily have any sort of relationship with the outcome. So would it be wise to use random forest to gather the most important features and then plug those features int... | Can a random forest be used for feature selection in multiple linear regression? Since RF can handle non-linearity but can't provide coefficients, would it be wise to use random forest to gather the most important features and then plug those features into a multiple linear regression model in order to obtain their coe... | What's the relationship between initial eigenvalues and sums of squared loadings in factor analysis? On the one hand I read in a comment that: You can't speak of "eigenvalues" after rotation, even orthogonal rotation. Perhaps you mean sum of squared loadings for a principal component, after rotation. When rota... | eng_Latn | 1,146 |
When is orthogonal projection compact? Let $M$ be a closed subspace of a Hilbert space $H$. Let $P$ be the orthogonal projection on $M$. I was told to find the eigenvalues and eigenvectors of $P$ and moreover say when it is compact. Since $M$ is closed we have $H\cong M\oplus M^\perp$ and this is an eigenspace decompo... | Showing that the orthogonal projection in a Hilbert space is compact iff the subspace is finite dimensional Suppose that we have a Hilbert Space $H$ and $M$ is a closed subspace of $H$. Let $T\colon H\rightarrow M$ be the orthogonal projection onto $M$. I have to show that $T$ is compact iff $M$ is finite dimensional... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,147 |
The first principal component line minimizes the sum of the squared perpendicular distances between each point and the line I am currently studying An Introduction to Statistical Learning, corrected 7th printing, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Chapter 6.3.1 Principal Components R... | Making sense of principal component analysis, eigenvectors & eigenvalues In today's pattern recognition class my professor talked about PCA, eigenvectors and eigenvalues. I understood the mathematics of it. If I'm asked to find eigenvalues etc. I'll do it correctly like a machine. But I didn't understand it. I didn't... | How should I interpret this residual plot? I am unable to interpret this graph. My dependent variable is total number of movie tickets that will be sold for a show. The independent variables are the number of days left before the show, seasonality dummy variables (day of week, month of year, holiday), price, tickets so... | eng_Latn | 1,148 |
Minimal polynomial of a matrix whose elements have a certain form Find the minimal polynomial of the $n$-dimensional matrix $(a_{ij})$ when the matrix elements $a_{ij}$ have the form $a_{ij} = u_i v_j.$ Let $A=uv^T$ where $u,v$ are column vectors. Then rank$(A)\leq$rank$(u)\leq1.$ So kernal$(A)\geq n-1.$ That is, t... | Determining possible minimal polynomials for a rank one linear operator I have come across a question about determining possible minimal polynomials for a rank one linear operator and I am wondering if I am using the correct proof method. I think that the facts needed to solve this problem come from the section on Nil... | Local polynomial regression: Why does the variance increase monotonically in the degree? How can I show that the variance of local polynomial regression is increasing with the degree of the polynomial (Exercise 6.3 in Elements of Statistical Learning, second edition)? This question has been asked but the answer just ... | eng_Latn | 1,149 |
Find Least‐Squares Solutions Using Linear Algebra | In data analysis, it is often a goal to find correlations for observed data, called trendlines. However, real life observations almost always yield inconsistent solutions to the matrix equation X β = y , {\displaystyle X{\boldsymbol {\beta }}={\mathbf {y} },} where y {\displayst... | There are many factors that have to be addressed before this question can be properly answered. Is the machine a vertical or horizontal machine? How any axes will be required? | eng_Latn | 1,150 |
Matrix of regression coefficients | Algorithm for minimization of sum of squares in regression packages | Regression without intercept: deriving $\hat{\beta}_1$ in least squares (no matrices) | eng_Latn | 1,151 |
How can logistic regression maximize AUC? | Does a logistic regression maximizing likelihood necessarily also maximize AUC over linear models? | Does a logistic regression maximizing likelihood necessarily also maximize AUC over linear models? | eng_Latn | 1,152 |
Solution for inverse square potential in $d=3$ spatial dimensions in quantum mechanics Can a particle in an inverse square potential $$V(r)=-1/r^{2}$$ in $d=3$ spatial dimensions be solved exactly? Also please explain me the physical significance of this potential in comparison with Coulomb potential? That problem was... | Radial Schrodinger equation with inverse power law potential Recently I read a about solving radial Schrodinger equation with inverse power law potential. Consider the radial Schrodinger equation(simply set $\mu=\hbar=1$): $$\left(-\frac{1}{2}\Delta+V(\mathbf{r})\right)\psi(\mathbf{r})=E\psi(\mathbf{r}).$$ A gives... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,153 |
Single particle passing through the double slit at a time | Slit screen and wave-particle duality | Why bother with the dual problem when fitting SVM? | eng_Latn | 1,154 |
Proving that the induced L1 and L-infinity matrix norms are duals of one another | Show that $(l_1)^* \cong l_{\infty}$ | Why L1 norm for sparse models | eng_Latn | 1,155 |
Reference for this claim: important features in data can be "hidden" in the higher PCA axes that are typically thrown out | Examples of PCA where PCs with low variance are "useful" | Using principal component analysis (PCA) for feature selection | eng_Latn | 1,156 |
Finding the associated matrix of a linear operator Let $V$ be a complex vector space of dimension $n$ with a scalar product, and let $u$ be an unitary vector in $V$. Let $H_u: V \to V$ be defined as $$H_u(v) = v - 2 \langle v,u \rangle u$$ for all $v \in V$. I need to find the minimal polynomial and the characteristi... | What's the associated matrix of this linear operator? Let $V$ be a complex vector space of dimension $n$ with a scalar product, and let $u$ be an unitary vector in $V$. Let $H_u: V \to V$ be defined as $$H_u(v) = v - 2 \langle v,u \rangle u$$ for all $v \in V$. I need to find the characteristic polynomial of this lin... | What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)? I've read a lot about PCA, including various tutorials and questions (such as , , , and ). The geometric problem that PCA is trying to optimize is clear to me: PCA tries to fin... | eng_Latn | 1,157 |
With data.table, is SD[which.max(Var1)] the fastest way to find the max of a group? | finding the index of a max value in R | Why is.vector on a data-frame doesn't return TRUE? | eng_Latn | 1,158 |
How to get statistics of all samples through statistics of subsamples? | Combining two covariance matrices | Subsample of a random sample: random sample? | eng_Latn | 1,159 |
How to get statistics of all samples through statistics of subsamples? | Combining two covariance matrices | Uploading an image from the web can leave paste broken in editor | eng_Latn | 1,160 |
Gradient of a matrix expression | How to take the gradient of the quadratic form? | Gradient Boosting for Linear Regression - why does it not work? | eng_Latn | 1,161 |
Decomposition rate of intact and injured piglet cadavers | Using Accumulated Degree-Days to Estimate the Postmortem Interval from Decomposed Human Remains | Scaling Factorization Machines with Parameter Server | eng_Latn | 1,162 |
R - is it ok to treat autocorrelation by randomly changing the order of variable I'm working on a multivariate linear regression. I performed a Durbin-Watson test to test for auto-correlation and found it in my model (p-value <0.05 of Durbin Watson test). I then randomly changed the order of the row of the dataframe... | durbinWatsonTest() in R for non time-series data I am trying to understand whether the Durbin-Watson test is meaningful at all when applied to regression data that have no temporal order (eg. blood pressure ~ bodyMassIndex + exercise). I would say no, as the autocorrelation should obviously vary with the order of the... | How to compute PCA scores from eigendecomposition of the covariance matrix? Given a data matrix $\mathbf X$ of $12 \times 7$ size with samples in rows and variables in columns, I have calculated centered data $\mathbf X_c$ by subtracting column means, and then computed covariance matrix as $\frac{1}{N-1} \mathbf X_c^\t... | eng_Latn | 1,163 |
The graphical intuiton of the LASSO in case p=2 | Graphical interpretation of LASSO | Prove that the expression cannot be a power of 2 | eng_Latn | 1,164 |
I am trying to learn filters. In the following circuit, the author says that negative and positive feedback are used. I couldn't understand what the circuit parts do. Actually I got the negative feedback highpass parts but the low pass part is a little different from normal filters because C2 doesn't bind with ground. | I have rigged up a notch filter from . See below: - Its the first circuit. Now I am trying to calculate its transfer function. I have calculated two equations, but I need the third one: (Vin-V2)/(1/SC1) = V2/R3 (Vin-V1)/R1 = (V1-Vo)/(R2+(1/SC2)) V1 and V2 are the potentials at the input of the op-Amp. | I am wondering why matrix factorization techniques in the machine learning domain almost always expect the provided matrix to be non-negative. What is the advantage of this constraint? Background: I want to use matrix factorization algorithms for a sparse user-item matrix containing positive and negative implicit feed... | eng_Latn | 1,165 |
Non static method cannot be referenced from a static context Binary Search | What is the reason behind "non-static method cannot be referenced from a static context"? | Positive semidefinite cone is generated by all rank-$1$ matrices. | eng_Latn | 1,166 |
COFI RANK Maximum Margin Matrix Factorization for Collaborative Ranking | Large Margin Methods for Structured and Interdependent Output Variables | Circulate shifted OFDM chirp waveform diversity design with digital beamforming for MIMO SAR | eng_Latn | 1,167 |
Given the Linear system $$Ax = b$$ where $A$ is an $s$-sparse ($s$ is the maximum number of non-zero entries in $A$), $k$-conditioned ($k$ is the ratio between the highest and the smallest eigenvalue) matrix of size $N$, how can I express the time complexity of CG method based on those three parameters? I have foun... | I have been trying to figure out the time complexity of the conjugate gradient method. I have to solve a system of linear equations given by $$Ax=b$$ where $A$ is a sparse, symmetric, positive definite matrix. What would be the time complexity of the conjugate gradient method? | So, I know that $\max(-X_{(1)},X_{(n)})$ is a sufficient statistic for the parameter $\theta$. But can I also say that $(X_{(1)},X_{(n)})$ are jointly sufficient for the parameter $\theta$ ? In other words, can a single parameter have jointly sufficient statistics? | eng_Latn | 1,168 |
Due to QFT books, we measure pole mass(physical mass) in experiments. From the Lagrangian point of view, renormalized mass is a parameter(in MS bar or some similar renormalization scheme that has an explicit renormalization scale) We fix renormalized parameters ( renormalized mass, renormalized couplings and ...) at s... | I am reading Schwarz QFT and I reached the mass renormalization part. So he introduces, after renormalization, a physical mass, defined as the pole of the renormalized propagator, and a renormalized mass which can be the physical mass, but can also take other values, depending on the subtraction schemes used. Are these... | To prevent overfitting people people add a regularization term (proportional to the squared sum of the parameters of the model) with a regularization parameter $\lambda$ to the cost function of linear regression. Is this parameter $\lambda$ the same as a lagrange multiplier? So is regularization the same as the method ... | eng_Latn | 1,169 |
Prove spectral radius of a primitive matrix is 1 | Proof that the largest eigenvalue of a stochastic matrix is $1$ | Rigid body simulation not interacting with objects that have an array modifier | eng_Latn | 1,170 |
MODELING AND SIMULATION OF MICRO-HYDRO POWER PLANTS FOR APPLICATIONS IN DISTRIBUTED GENERATION | Basic Modeling and Simulation Tool for Analysis of Hydraulic Transients in Hydroelectric Power Plants | Efficient Structured Multifrontal Factorization for General Large Sparse Matrices | yue_Hant | 1,171 |
Meaning of "$\exp[ \cdot ]$" in mathematical equations I am reading book "Fuzzy Logic With Engineering Applications, Wiley" written by Timothy J. Ross. I am reading chapter 7 and in this chapter, "Batch Least Squares Algoritm" has been defined. It illustrates the development of a nonlinear fuzzy model for the data in T... | What is the meaning of $\exp(\,\cdot\,)$? What is the meaning of the notation $\exp(\text{expression})$ ? I think that it's something of the form $a^\text{expression}$ but does it mean that the base $a=e$ or can it be any base? | Interpretation of LASSO regression coefficients I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise regression and why not to use it. I've been reading into LASSO regress... | eng_Latn | 1,172 |
Does L1 regularization (Lasso) always leads to feature reduction? | Why does the Lasso provide Variable Selection? | When will L1 regularization work better than L2 and vice versa? | eng_Latn | 1,173 |
Time Complexity line by line | Time complexity of nested for-loop | Collinearity diagnostics problematic only when the interaction term is included | eng_Latn | 1,174 |
Maximum size square sub-matrix with all 1s | Puzzle: Find largest rectangle (maximal rectangle problem) | Subsetting matrices | eng_Latn | 1,175 |
PCA scores in my own implementation have different sign from the ones computed in R | Is the sign of principal components meaningless? | $\mathbb R$ has the same cardinality of any interval | eng_Latn | 1,176 |
Gradient of an $n$-variate quadratic form | How to take the gradient of the quadratic form? | Gradient Boosting for Linear Regression - why does it not work? | eng_Latn | 1,177 |
Principal component analysis- covariance or correlation matrix | PCA on correlation or covariance? | Why is the covariant derivative of the metric tensor zero? | eng_Latn | 1,178 |
Accepted answer not ranked first | Accepted answer appearing below top voted | Positive semidefinite cone is generated by all rank-$1$ matrices. | eng_Latn | 1,179 |
How to transform symmetric matrix to diagonal? | $\mathbf L\mathbf D\mathbf L^\top$ Cholesky decomposition | Eigenvectors of real symmetric matrices are orthogonal | eng_Latn | 1,180 |
How to compute F-statistics for each features of regression models in glmnet? | Lasso and statistical signficance of selected variables | Latexmk can't see a dependency on a .fmt format file | eng_Latn | 1,181 |
Why is L2 regression good for handling multicollinearity? | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity? | L1 regression estimates median whereas L2 regression estimates mean? | eng_Latn | 1,182 |
Solving System of equations and L-U decomposition of matrices | When does a Square Matrix have an LU Decomposition? | When does a Square Matrix have an LU Decomposition? | eng_Latn | 1,183 |
Eigenvectors computed by Matlab's princomp() and eig() have different signs | Does the sign of scores or of loadings in PCA or FA have a meaning? May I reverse the sign? | Every principal ideal domain satisfies ACCP. | eng_Latn | 1,184 |
How does $\vec{\beta}=(H^TH)^{-1}H^T\vec{y}$ equivalent to least squares criteria for evaluating splines? | What algorithm is used in linear regression? | "strlen(s1) - strlen(s2)" is never less than zero | eng_Latn | 1,185 |
Extract matrix from a system of linear equations | How to read off coefficients of tensor-like expression in a speedy way? | Regression without intercept: deriving $\hat{\beta}_1$ in least squares (no matrices) | eng_Latn | 1,186 |
Problems with SEM: Non-positive definite matrix | "matrix is not positive definite" - even when highly correlated variables are removed | Positive semidefinite cone is generated by all rank-$1$ matrices. | eng_Latn | 1,187 |
When using principal components as predictors in linear regression, PC1 comes out not significant | How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)? | Every principal ideal domain satisfies ACCP. | eng_Latn | 1,188 |
Why do prcomp() and eigen(cov()) in R return different signs of PCA eigenvectors? | PCA: Eigenvectors of opposite sign and not being able to compute eigenvectors with `solve` in R | Every principal ideal domain satisfies ACCP. | eng_Latn | 1,189 |
Derivation of linear discriminant analysis (LDA) decision boundary | Linear Discriminant Analysis for $p=1$ | Why with two classes, LDA gives only one dimension? | eng_Latn | 1,190 |
Why avoid stepwise regression? | Algorithms for automatic model selection | Why does the degree of freedom of SSR equals 1 in simple linear regression | eng_Latn | 1,191 |
Why is there only one discriminant axis when doing Linear Discriminant Analysis on two classes? | Why with two classes, LDA gives only one dimension? | Linear Discriminant Analysis for $p=1$ | eng_Latn | 1,192 |
Variability of K SVD components | Relationship between SVD and PCA. How to use SVD to perform PCA? | Recovering eigenvectors from SVD | eng_Latn | 1,193 |
Why maximum likelihood estimation is same with minimizing cross-entropy? | the relationship between maximizing the likelihood and minimizing the cross-entropy | I'm not able to get access to $wpdb | eng_Latn | 1,194 |
what is lanczos method | 1 Introduction The Lanczos method [15] is widely used for ï¬nding a small number of extreme eigenval-ues and their associated eigenvectors of a symmetric matrix (or Hermitian matrix in thecomplex case).6.9)This bound, too, could be much bigger than the one of (5.1) because of one or more ζ j with k â i Ä j Ä â ... | Here is how to make small images larger in Gimp without losing quality. Open the image you want to resize in Gimp. Simply go to Image » Scale Image. Enter your desired dimensions. Under the Quality section choose Sinc (Lanczos3) as Interpolation method and click on the Scale Image button. | eng_Latn | 1,195 |
what is the decomposition | Decomposition is the natural process of dead animal or plant tissue being rotted or broken down. This process is carried out by invertebrates, fungi and bacteria. The result of decomposition is that the building blocks required for life can be recycled. Left: The body of a dead rabbit after several weeks of decompositi... | Linear Algebra: How can a Cholesky decomposition of (I+S) be efficiently found given that of S=R'R? What is the intuition for a minor of a matrix? What is the role of the Cholesky decomposition in finding multivariate normal PDF? | eng_Latn | 1,196 |
Cropping sf object in R? | Crop simple features object in R | Matrix doesn't shrink when put in fraction. | eng_Latn | 1,197 |
Why the "Sum" function becomes extremely slow at a specific size of matrix? How to AVOID it? | Sudden increase in timing when summing over 250 entries | Sudden increase in timing when summing over 250 entries | eng_Latn | 1,198 |
Subsetting while reading in R | how to read huge csv file into R by row condition? | Subsetting matrices | eng_Latn | 1,199 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.