Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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32,602 | Subspace Restricted Boltzmann Machine | cs.LG | The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order
Boltzmann machine where multiplicative interactions are between one visible and
two hidden units. There are two kinds of hidden units, namely, gate units and
subspace units. The subspace units reflect variations of a pattern in data and
the gate u... | computer science |
32,603 | A feature construction framework based on outlier detection and
discriminative pattern mining | cs.LG | No matter the expressive power and sophistication of supervised learning
algorithms, their effectiveness is restricted by the features describing the
data. This is not a new insight in ML and many methods for feature selection,
transformation, and construction have been developed. But while this is
on-going for general... | computer science |
32,604 | Exploiting Smoothness in Statistical Learning, Sequential Prediction,
and Stochastic Optimization | cs.LG | In the last several years, the intimate connection between convex
optimization and learning problems, in both statistical and sequential
frameworks, has shifted the focus of algorithmic machine learning to examine
this interplay. In particular, on one hand, this intertwinement brings forward
new challenges in reassessm... | computer science |
32,605 | A Fast Synchronization Clustering Algorithm | cs.LG | This paper presents a Fast Synchronization Clustering algorithm (FSynC),
which is an improved version of SynC algorithm. In order to decrease the time
complexity of the original SynC algorithm, we combine grid cell partitioning
method and Red-Black tree to construct the near neighbor point set of every
point. By simula... | computer science |
32,606 | Chasing Ghosts: Competing with Stateful Policies | cs.LG | We consider sequential decision making in a setting where regret is measured
with respect to a set of stateful reference policies, and feedback is limited
to observing the rewards of the actions performed (the so called "bandit"
setting). If either the reference policies are stateless rather than stateful,
or the feedb... | computer science |
32,607 | A Hash-based Co-Clustering Algorithm for Categorical Data | cs.LG | Many real-life data are described by categorical attributes without a
pre-classification. A common data mining method used to extract information
from this type of data is clustering. This method group together the samples
from the data that are more similar than all other samples. But, categorical
data pose a challeng... | computer science |
32,608 | How Auto-Encoders Could Provide Credit Assignment in Deep Networks via
Target Propagation | cs.LG | We propose to exploit {\em reconstruction} as a layer-local training signal
for deep learning. Reconstructions can be propagated in a form of target
propagation playing a role similar to back-propagation but helping to reduce
the reliance on derivatives in order to perform credit assignment across many
levels of possib... | computer science |
32,609 | DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning
with Applications to Neuroimages | cs.LG | With advances in data collection technologies, tensor data is assuming
increasing prominence in many applications and the problem of supervised tensor
learning has emerged as a topic of critical significance in the data mining and
machine learning community. Conventional methods for supervised tensor learning
mainly fo... | computer science |
32,610 | Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically
Triggered Arms | cs.LG | We define a general framework for a large class of combinatorial multi-armed
bandit (CMAB) problems, where subsets of base arms with unknown distributions
form super arms. In each round, a super arm is played and the base arms
contained in the super arm are played and their outcomes are observed. We
further consider th... | computer science |
32,611 | Data classification using the Dempster-Shafer method | cs.LG | In this paper, the Dempster-Shafer method is employed as the theoretical
basis for creating data classification systems. Testing is carried out using
three popular (multiple attribute) benchmark datasets that have two, three and
four classes. In each case, a subset of the available data is used for training
to establis... | computer science |
32,612 | Solving the Problem of the K Parameter in the KNN Classifier Using an
Ensemble Learning Approach | cs.LG | This paper presents a new solution for choosing the K parameter in the
k-nearest neighbor (KNN) algorithm, the solution depending on the idea of
ensemble learning, in which a weak KNN classifier is used each time with a
different K, starting from one to the square root of the size of the training
set. The results of th... | computer science |
32,613 | Dimensionality Invariant Similarity Measure | cs.LG | This paper presents a new similarity measure to be used for general tasks
including supervised learning, which is represented by the K-nearest neighbor
classifier (KNN). The proposed similarity measure is invariant to large
differences in some dimensions in the feature space. The proposed metric is
proved mathematicall... | computer science |
32,614 | Domain Transfer Structured Output Learning | cs.LG | In this paper, we propose the problem of domain transfer structured output
learn- ing and the first solution to solve it. The problem is defined on two
different data domains sharing the same input and output spaces, named as
source domain and target domain. The outputs are structured, and for the data
samples of the s... | computer science |
32,615 | Novel Methods for Activity Classification and Occupany Prediction
Enabling Fine-grained HVAC Control | cs.LG | Much of the energy consumption in buildings is due to HVAC systems, which has
motivated several recent studies on making these systems more energy-
efficient. Occupancy and activity are two important aspects, which need to be
correctly estimated for optimal HVAC control. However, state-of-the-art methods
to estimate oc... | computer science |
32,616 | Non-Convex Boosting Overcomes Random Label Noise | cs.LG | The sensitivity of Adaboost to random label noise is a well-studied problem.
LogitBoost, BrownBoost and RobustBoost are boosting algorithms claimed to be
less sensitive to noise than AdaBoost. We present the results of experiments
evaluating these algorithms on both synthetic and real datasets. We compare the
performan... | computer science |
32,617 | Metric Learning for Temporal Sequence Alignment | cs.LG | In this paper, we propose to learn a Mahalanobis distance to perform
alignment of multivariate time series. The learning examples for this task are
time series for which the true alignment is known. We cast the alignment
problem as a structured prediction task, and propose realistic losses between
alignments for which ... | computer science |
32,618 | Consensus-Based Modelling using Distributed Feature Construction | cs.LG | A particularly successful role for Inductive Logic Programming (ILP) is as a
tool for discovering useful relational features for subsequent use in a
predictive model. Conceptually, the case for using ILP to construct relational
features rests on treating these features as functions, the automated discovery
of which nec... | computer science |
32,619 | Active Metric Learning from Relative Comparisons | cs.LG | This work focuses on active learning of distance metrics from relative
comparison information. A relative comparison specifies, for a data point
triplet $(x_i,x_j,x_k)$, that instance $x_i$ is more similar to $x_j$ than to
$x_k$. Such constraints, when available, have been shown to be useful toward
defining appropriate... | computer science |
32,620 | A Mixtures-of-Experts Framework for Multi-Label Classification | cs.LG | We develop a novel probabilistic approach for multi-label classification that
is based on the mixtures-of-experts architecture combined with recently
introduced conditional tree-structured Bayesian networks. Our approach captures
different input-output relations from multi-label data using the efficient
tree-structured... | computer science |
32,621 | Predictive Capacity of Meteorological Data - Will it rain tomorrow | cs.LG | With the availability of high precision digital sensors and cheap storage
medium, it is not uncommon to find large amounts of data collected on almost
all measurable attributes, both in nature and man-made habitats. Weather in
particular has been an area of keen interest for researchers to develop more
accurate and rel... | computer science |
32,622 | Learning and approximation capability of orthogonal super greedy
algorithm | cs.LG | We consider the approximation capability of orthogonal super greedy
algorithms (OSGA) and its applications in supervised learning. OSGA is
concerned with selecting more than one atoms in each iteration step, which, of
course, greatly reduces the computational burden when compared with the
conventional orthogonal greedy... | computer science |
32,623 | Efficient Feature Group Sequencing for Anytime Linear Prediction | cs.LG | We consider \textit{anytime} linear prediction in the common machine learning
setting, where features are in groups that have costs. We achieve anytime (or
interruptible) predictions by sequencing the computation of feature groups and
reporting results using the computed features at interruption. We extend
Orthogonal M... | computer science |
32,624 | A Survey on Soft Subspace Clustering | cs.LG | Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by t... | computer science |
32,625 | Transfer Prototype-based Fuzzy Clustering | cs.LG | The traditional prototype based clustering methods, such as the well-known
fuzzy c-mean (FCM) algorithm, usually need sufficient data to find a good
clustering partition. If the available data is limited or scarce, most of the
existing prototype based clustering algorithms will no longer be effective.
While the data fo... | computer science |
32,626 | The Information Theoretically Efficient Model (ITEM): A model for
computerized analysis of large datasets | cs.LG | This document discusses the Information Theoretically Efficient Model (ITEM),
a computerized system to generate an information theoretically efficient
multinomial logistic regression from a general dataset. More specifically, this
model is designed to succeed even where the logit transform of the dependent
variable is ... | computer science |
32,627 | Best-Arm Identification in Linear Bandits | cs.LG | We study the best-arm identification problem in linear bandit, where the
rewards of the arms depend linearly on an unknown parameter $\theta^*$ and the
objective is to return the arm with the largest reward. We characterize the
complexity of the problem and introduce sample allocation strategies that pull
arms to ident... | computer science |
32,628 | A Boosting Framework on Grounds of Online Learning | cs.LG | By exploiting the duality between boosting and online learning, we present a
boosting framework which proves to be extremely powerful thanks to employing
the vast knowledge available in the online learning area. Using this framework,
we develop various algorithms to address multiple practically and theoretically
intere... | computer science |
32,629 | A Semidefinite Programming Based Search Strategy for Feature Selection
with Mutual Information Measure | cs.LG | Feature subset selection, as a special case of the general subset selection
problem, has been the topic of a considerable number of studies due to the
growing importance of data-mining applications. In the feature subset selection
problem there are two main issues that need to be addressed: (i) Finding an
appropriate m... | computer science |
32,630 | Maximum mutual information regularized classification | cs.LG | In this paper, a novel pattern classification approach is proposed by
regularizing the classifier learning to maximize mutual information between the
classification response and the true class label. We argue that, with the
learned classifier, the uncertainty of the true class label of a data sample
should be reduced b... | computer science |
32,631 | Cognitive Learning of Statistical Primary Patterns via Bayesian Network | cs.LG | In cognitive radio (CR) technology, the trend of sensing is no longer to only
detect the presence of active primary users. A large number of applications
demand for more comprehensive knowledge on primary user behaviors in spatial,
temporal, and frequency domains. To satisfy such requirements, we study the
statistical ... | computer science |
32,632 | Efficient multivariate sequence classification | cs.LG | Kernel-based approaches for sequence classification have been successfully
applied to a variety of domains, including the text categorization, image
classification, speech analysis, biological sequence analysis, time series and
music classification, where they show some of the most accurate results.
Typical kernel fu... | computer science |
32,633 | Generalized Laguerre Reduction of the Volterra Kernel for Practical
Identification of Nonlinear Dynamic Systems | cs.LG | The Volterra series can be used to model a large subset of nonlinear, dynamic
systems. A major drawback is the number of coefficients required model such
systems. In order to reduce the number of required coefficients, Laguerre
polynomials are used to estimate the Volterra kernels. Existing literature
proposes algorith... | computer science |
32,634 | Online Ranking with Top-1 Feedback | cs.LG | We consider a setting where a system learns to rank a fixed set of $m$ items.
The goal is produce good item rankings for users with diverse interests who
interact online with the system for $T$ rounds. We consider a novel top-$1$
feedback model: at the end of each round, the relevance score for only the top
ranked obje... | computer science |
32,635 | Stochastic Discriminative EM | cs.LG | Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for
discriminative training of probabilistic generative models belonging to the
exponential family. In this work, we introduce and justify this algorithm as a
stochastic natural gradient descent method, i.e. a method which accounts for
the information g... | computer science |
32,636 | Learning manifold to regularize nonnegative matrix factorization | cs.LG | Inthischapterwediscusshowtolearnanoptimalmanifoldpresentationto regularize
nonegative matrix factorization (NMF) for data representation problems.
NMF,whichtriestorepresentanonnegativedatamatrixasaproductoftwolowrank
nonnegative matrices, has been a popular method for data representation due to
its ability to explore t... | computer science |
32,637 | A Logic-based Approach to Generatively Defined Discriminative Modeling | cs.LG | Conditional random fields (CRFs) are usually specified by graphical models
but in this paper we propose to use probabilistic logic programs and specify
them generatively. Our intension is first to provide a unified approach to CRFs
for complex modeling through the use of a Turing complete language and second
to offer a... | computer science |
32,638 | Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of
Convex Sets | cs.LG | Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique
for simultaneously discovering group and within-group sparse patterns by using
a combination of the $\ell_1$ and $\ell_2$ norms. However, in large-scale
applications, the complexity of the regularizers entails great computational
challenges. ... | computer science |
32,639 | Learning a hyperplane regressor by minimizing an exact bound on the VC
dimension | cs.LG | The capacity of a learning machine is measured by its Vapnik-Chervonenkis
dimension, and learning machines with a low VC dimension generalize better. It
is well known that the VC dimension of SVMs can be very large or unbounded,
even though they generally yield state-of-the-art learning performance. In this
paper, we s... | computer science |
32,640 | Naive Bayes and Text Classification I - Introduction and Theory | cs.LG | Naive Bayes classifiers, a family of classifiers that are based on the
popular Bayes' probability theorem, are known for creating simple yet well
performing models, especially in the fields of document classification and
disease prediction. In this article, we will look at the main concepts of naive
Bayes classificatio... | computer science |
32,641 | An Overview of General Performance Metrics of Binary Classifier Systems | cs.LG | This document provides a brief overview of different metrics and terminology
that is used to measure the performance of binary classification systems. | computer science |
32,642 | Feature Selection Based on Confidence Machine | cs.LG | In machine learning and pattern recognition, feature selection has been a hot
topic in the literature. Unsupervised feature selection is challenging due to
the loss of labels which would supply the related information.How to define an
appropriate metric is the key for feature selection. We propose a filter method
for u... | computer science |
32,643 | Cosine Similarity Measure According to a Convex Cost Function | cs.LG | In this paper, we describe a new vector similarity measure associated with a
convex cost function. Given two vectors, we determine the surface normals of
the convex function at the vectors. The angle between the two surface normals
is the similarity measure. Convex cost function can be the negative entropy
function, to... | computer science |
32,644 | Differentially- and non-differentially-private random decision trees | cs.LG | We consider supervised learning with random decision trees, where the tree
construction is completely random. The method is popularly used and works well
in practice despite the simplicity of the setting, but its statistical
mechanism is not yet well-understood. In this paper we provide strong
theoretical guarantees re... | computer science |
32,645 | Notes on using Determinantal Point Processes for Clustering with
Applications to Text Clustering | cs.LG | In this paper, we compare three initialization schemes for the KMEANS
clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and
3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k
needs to be set by the user of the algorithms. (Kang 2013) recently proposed a
novel use of dete... | computer science |
32,646 | Feature Selection through Minimization of the VC dimension | cs.LG | Feature selection involes identifying the most relevant subset of input
features, with a view to improving generalization of predictive models by
reducing overfitting. Directly searching for the most relevant combination of
attributes is NP-hard. Variable selection is of critical importance in many
applications, such a... | computer science |
32,647 | Fast Learning of Relational Dependency Networks | cs.LG | A Relational Dependency Network (RDN) is a directed graphical model widely
used for multi-relational data. These networks allow cyclic dependencies,
necessary to represent relational autocorrelations. We describe an approach for
learning both the RDN's structure and its parameters, given an input relational
database: F... | computer science |
32,648 | Global Bandits with Holder Continuity | cs.LG | Standard Multi-Armed Bandit (MAB) problems assume that the arms are
independent. However, in many application scenarios, the information obtained
by playing an arm provides information about the remainder of the arms. Hence,
in such applications, this informativeness can and should be exploited to
enable faster converg... | computer science |
32,649 | Notes on Noise Contrastive Estimation and Negative Sampling | cs.LG | Estimating the parameters of probabilistic models of language such as maxent
models and probabilistic neural models is computationally difficult since it
involves evaluating partition functions by summing over an entire vocabulary,
which may be millions of word types in size. Two closely related
strategies---noise cont... | computer science |
32,650 | NICE: Non-linear Independent Components Estimation | cs.LG | We propose a deep learning framework for modeling complex high-dimensional
densities called Non-linear Independent Component Estimation (NICE). It is
based on the idea that a good representation is one in which the data has a
distribution that is easy to model. For this purpose, a non-linear
deterministic transformatio... | computer science |
32,651 | Learning Mixtures of Ranking Models | cs.LG | This work concerns learning probabilistic models for ranking data in a
heterogeneous population. The specific problem we study is learning the
parameters of a Mallows Mixture Model. Despite being widely studied, current
heuristics for this problem do not have theoretical guarantees and can get
stuck in bad local optima... | computer science |
32,652 | Factorbird - a Parameter Server Approach to Distributed Matrix
Factorization | cs.LG | We present Factorbird, a prototype of a parameter server approach for
factorizing large matrices with Stochastic Gradient Descent-based algorithms.
We designed Factorbird to meet the following desiderata: (a) scalability to
tall and wide matrices with dozens of billions of non-zeros, (b) extensibility
to different kind... | computer science |
32,653 | CUR Algorithm for Partially Observed Matrices | cs.LG | CUR matrix decomposition computes the low rank approximation of a given
matrix by using the actual rows and columns of the matrix. It has been a very
useful tool for handling large matrices. One limitation with the existing
algorithms for CUR matrix decomposition is that they need an access to the {\it
full} matrix, a ... | computer science |
32,654 | Eigenvectors of Orthogonally Decomposable Functions | cs.LG | The Eigendecomposition of quadratic forms (symmetric matrices) guaranteed by
the spectral theorem is a foundational result in applied mathematics. Motivated
by a shared structure found in inferential problems of recent interest---namely
orthogonal tensor decompositions, Independent Component Analysis (ICA), topic
model... | computer science |
32,655 | On the Information Theoretic Limits of Learning Ising Models | cs.LG | We provide a general framework for computing lower-bounds on the sample
complexity of recovering the underlying graphs of Ising models, given i.i.d
samples. While there have been recent results for specific graph classes, these
involve fairly extensive technical arguments that are specialized to each
specific graph cla... | computer science |
32,656 | Efficient Representations for Life-Long Learning and Autoencoding | cs.LG | It has been a long-standing goal in machine learning, as well as in AI more
generally, to develop life-long learning systems that learn many different
tasks over time, and reuse insights from tasks learned, "learning to learn" as
they do so. In this work we pose and provide efficient algorithms for several
natural theo... | computer science |
32,657 | A Hybrid Recurrent Neural Network For Music Transcription | cs.LG | We investigate the problem of incorporating higher-level symbolic score-like
information into Automatic Music Transcription (AMT) systems to improve their
performance. We use recurrent neural networks (RNNs) and their variants as
music language models (MLMs) and present a generative architecture for
combining these mod... | computer science |
32,658 | Online Collaborative-Filtering on Graphs | cs.LG | A common phenomena in modern recommendation systems is the use of feedback
from one user to infer the `value' of an item to other users. This results in
an exploration vs. exploitation trade-off, in which items of possibly low value
have to be presented to users in order to ascertain their value. Existing
approaches to... | computer science |
32,659 | A chain rule for the expected suprema of Gaussian processes | cs.LG | The expected supremum of a Gaussian process indexed by the image of an index
set under a function class is bounded in terms of separate properties of the
index set and the function class. The bound is relevant to the estimation of
nonlinear transformations or the analysis of learning algorithms whenever
hypotheses are ... | computer science |
32,660 | Bounded Regret for Finite-Armed Structured Bandits | cs.LG | We study a new type of K-armed bandit problem where the expected return of
one arm may depend on the returns of other arms. We present a new algorithm for
this general class of problems and show that under certain circumstances it is
possible to achieve finite expected cumulative regret. We also give
problem-dependent ... | computer science |
32,661 | Greedy metrics in orthogonal greedy learning | cs.LG | Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a
new atom from a dictionary via the steepest gradient descent and build the
estimator via orthogonal projecting the target function to the space spanned by
the selected atoms in each greedy step. Here, "greed" means choosing a new atom
according ... | computer science |
32,662 | Minimal Realization Problems for Hidden Markov Models | cs.LG | Consider a stationary discrete random process with alphabet size d, which is
assumed to be the output process of an unknown stationary Hidden Markov Model
(HMM). Given the joint probabilities of finite length strings of the process,
we are interested in finding a finite state generative model to describe the
entire pro... | computer science |
32,663 | Sample-targeted clinical trial adaptation | cs.LG | Clinical trial adaptation refers to any adjustment of the trial protocol
after the onset of the trial. The main goal is to make the process of
introducing new medical interventions to patients more efficient by reducing
the cost and the time associated with evaluating their safety and efficacy. The
principal question i... | computer science |
32,664 | Differentially Private Algorithms for Empirical Machine Learning | cs.LG | An important use of private data is to build machine learning classifiers.
While there is a burgeoning literature on differentially private classification
algorithms, we find that they are not practical in real applications due to two
reasons. First, existing differentially private classifiers provide poor
accuracy on ... | computer science |
32,665 | No-Regret Learnability for Piecewise Linear Losses | cs.LG | In the convex optimization approach to online regret minimization, many
methods have been developed to guarantee a $O(\sqrt{T})$ bound on regret for
subdifferentiable convex loss functions with bounded subgradients, by using a
reduction to linear loss functions. This suggests that linear loss functions
tend to be the h... | computer science |
32,666 | Compound Rank-k Projections for Bilinear Analysis | cs.LG | In many real-world applications, data are represented by matrices or
high-order tensors. Despite the promising performance, the existing
two-dimensional discriminant analysis algorithms employ a single projection
model to exploit the discriminant information for projection, making the model
less flexible. In this paper... | computer science |
32,667 | Semi-supervised Feature Analysis by Mining Correlations among Multiple
Tasks | cs.LG | In this paper, we propose a novel semi-supervised feature selection framework
by mining correlations among multiple tasks and apply it to different
multimedia applications. Instead of independently computing the importance of
features for each task, our algorithm leverages shared knowledge from multiple
related tasks, ... | computer science |
32,668 | A Convex Sparse PCA for Feature Analysis | cs.LG | Principal component analysis (PCA) has been widely applied to dimensionality
reduction and data pre-processing for different applications in engineering,
biology and social science. Classical PCA and its variants seek for linear
projections of the original variables to obtain a low dimensional feature
representation wi... | computer science |
32,669 | Balanced k-Means and Min-Cut Clustering | cs.LG | Clustering is an effective technique in data mining to generate groups that
are the matter of interest. Among various clustering approaches, the family of
k-means algorithms and min-cut algorithms gain most popularity due to their
simplicity and efficacy. The classical k-means algorithm partitions a number of
data poin... | computer science |
32,670 | Improved Spectral Clustering via Embedded Label Propagation | cs.LG | Spectral clustering is a key research topic in the field of machine learning
and data mining. Most of the existing spectral clustering algorithms are built
upon Gaussian Laplacian matrices, which are sensitive to parameters. We propose
a novel parameter free, distance consistent Locally Linear Embedding. The
proposed d... | computer science |
32,671 | Structure Regularization for Structured Prediction: Theories and
Experiments | cs.LG | While there are many studies on weight regularization, the study on structure
regularization is rare. Many existing systems on structured prediction focus on
increasing the level of structural dependencies within the model. However, this
trend could have been misdirected, because our study suggests that complex
structu... | computer science |
32,672 | Revenue Optimization in Posted-Price Auctions with Strategic Buyers | cs.LG | We study revenue optimization learning algorithms for posted-price auctions
with strategic buyers. We analyze a very broad family of monotone regret
minimization algorithms for this problem, which includes the previously best
known algorithm, and show that no algorithm in that family admits a strategic
regret more favo... | computer science |
32,673 | A Convex Formulation for Spectral Shrunk Clustering | cs.LG | Spectral clustering is a fundamental technique in the field of data mining
and information processing. Most existing spectral clustering algorithms
integrate dimensionality reduction into the clustering process assisted by
manifold learning in the original space. However, the manifold in
reduced-dimensional subspace is... | computer science |
32,674 | Accelerated Parallel Optimization Methods for Large Scale Machine
Learning | cs.LG | The growing amount of high dimensional data in different machine learning
applications requires more efficient and scalable optimization algorithms. In
this work, we consider combining two techniques, parallelism and Nesterov's
acceleration, to design faster algorithms for L1-regularized loss. We first
simplify BOOM, a... | computer science |
32,675 | Worst-Case Linear Discriminant Analysis as Scalable Semidefinite
Feasibility Problems | cs.LG | In this paper, we propose an efficient semidefinite programming (SDP)
approach to worst-case linear discriminant analysis (WLDA). Compared with the
traditional LDA, WLDA considers the dimensionality reduction problem from the
worst-case viewpoint, which is in general more robust for classification.
However, the origina... | computer science |
32,676 | Graph Sensitive Indices for Comparing Clusterings | cs.LG | This report discusses two new indices for comparing clusterings of a set of
points. The motivation for looking at new ways for comparing clusterings stems
from the fact that the existing clustering indices are based on set cardinality
alone and do not consider the positions of data points. The new indices,
namely, the ... | computer science |
32,677 | Guaranteed Matrix Completion via Non-convex Factorization | cs.LG | Matrix factorization is a popular approach for large-scale matrix completion.
The optimization formulation based on matrix factorization can be solved very
efficiently by standard algorithms in practice. However, due to the
non-convexity caused by the factorization model, there is a limited theoretical
understanding of... | computer science |
32,678 | The Loss Surfaces of Multilayer Networks | cs.LG | We study the connection between the highly non-convex loss function of a
simple model of the fully-connected feed-forward neural network and the
Hamiltonian of the spherical spin-glass model under the assumptions of: i)
variable independence, ii) redundancy in network parametrization, and iii)
uniformity. These assumpt... | computer science |
32,679 | Easy Hyperparameter Search Using Optunity | cs.LG | Optunity is a free software package dedicated to hyperparameter optimization.
It contains various types of solvers, ranging from undirected methods to direct
search, particle swarm and evolutionary optimization. The design focuses on
ease of use, flexibility, code clarity and interoperability with existing
software in ... | computer science |
32,680 | Fast Rates by Transferring from Auxiliary Hypotheses | cs.LG | In this work we consider the learning setting where, in addition to the
training set, the learner receives a collection of auxiliary hypotheses
originating from other tasks. We focus on a broad class of ERM-based linear
algorithms that can be instantiated with any non-negative smooth loss function
and any strongly conv... | computer science |
32,681 | A parallel sampling based clustering | cs.LG | The problem of automatically clustering data is an age old problem. People
have created numerous algorithms to tackle this problem. The execution time of
any of this algorithm grows with the number of input points and the number of
cluster centers required. To reduce the number of input points we could average
the poin... | computer science |
32,682 | Consistent optimization of AMS by logistic loss minimization | cs.LG | In this paper, we theoretically justify an approach popular among
participants of the Higgs Boson Machine Learning Challenge to optimize
approximate median significance (AMS). The approach is based on the following
two-stage procedure. First, a real-valued function is learned by minimizing a
surrogate loss for binary c... | computer science |
32,683 | Theano-based Large-Scale Visual Recognition with Multiple GPUs | cs.LG | In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012)
implementation and its naive data parallelism on multiple GPUs. Our performance
on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014)
run on 1 GPU. To the best of our knowledge, this is the first open-source
Python-bas... | computer science |
32,684 | Accurate Streaming Support Vector Machines | cs.LG | A widely-used tool for binary classification is the Support Vector Machine
(SVM), a supervised learning technique that finds the "maximum margin" linear
separator between the two classes. While SVMs have been well studied in the
batch (offline) setting, there is considerably less work on the streaming
(online) setting,... | computer science |
32,685 | Sequential Labeling with online Deep Learning | cs.LG | Deep learning has attracted great attention recently and yielded the state of
the art performance in dimension reduction and classification problems.
However, it cannot effectively handle the structured output prediction, e.g.
sequential labeling. In this paper, we propose a deep learning structure, which
can learn dis... | computer science |
32,686 | An Evaluation of Support Vector Machines as a Pattern Recognition Tool | cs.LG | The purpose of this report is in examining the generalization performance of
Support Vector Machines (SVM) as a tool for pattern recognition and object
classification. The work is motivated by the growing popularity of the method
that is claimed to guarantee a good generalization performance for the task in
hand. The m... | computer science |
32,687 | Max-Margin based Discriminative Feature Learning | cs.LG | In this paper, we propose a new max-margin based discriminative feature
learning method. Specifically, we aim at learning a low-dimensional feature
representation, so as to maximize the global margin of the data and make the
samples from the same class as close as possible. In order to enhance the
robustness to noise, ... | computer science |
32,688 | Learning from Data with Heterogeneous Noise using SGD | cs.LG | We consider learning from data of variable quality that may be obtained from
different heterogeneous sources. Addressing learning from heterogeneous data in
its full generality is a challenging problem. In this paper, we adopt instead a
model in which data is observed through heterogeneous noise, where the noise
level ... | computer science |
32,689 | Dynamic Structure Embedded Online Multiple-Output Regression for Stream
Data | cs.LG | Online multiple-output regression is an important machine learning technique
for modeling, predicting, and compressing multi-dimensional correlated data
streams. In this paper, we propose a novel online multiple-output regression
method, called MORES, for stream data. MORES can \emph{dynamically} learn the
structure of... | computer science |
32,690 | Large Scale Distributed Distance Metric Learning | cs.LG | In large scale machine learning and data mining problems with high feature
dimensionality, the Euclidean distance between data points can be
uninformative, and Distance Metric Learning (DML) is often desired to learn a
proper similarity measure (using side information such as example data pairs
being similar or dissimi... | computer science |
32,691 | Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good
Similarity Functions | cs.LG | The notion of metric plays a key role in machine learning problems such as
classification, clustering or ranking. However, it is worth noting that there
is a severe lack of theoretical guarantees that can be expected on the
generalization capacity of the classifier associated to a given metric. The
theoretical framewor... | computer science |
32,692 | Fast Label Embeddings via Randomized Linear Algebra | cs.LG | Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and ... | computer science |
32,693 | Hot Swapping for Online Adaptation of Optimization Hyperparameters | cs.LG | We describe a general framework for online adaptation of optimization
hyperparameters by `hot swapping' their values during learning. We investigate
this approach in the context of adaptive learning rate selection using an
explore-exploit strategy from the multi-armed bandit literature. Experiments on
a benchmark neura... | computer science |
32,694 | Understanding Minimum Probability Flow for RBMs Under Various Kinds of
Dynamics | cs.LG | Energy-based models are popular in machine learning due to the elegance of
their formulation and their relationship to statistical physics. Among these,
the Restricted Boltzmann Machine (RBM), and its staple training algorithm
contrastive divergence (CD), have been the prototype for some recent
advancements in the unsu... | computer science |
32,695 | Adam: A Method for Stochastic Optimization | cs.LG | We introduce Adam, an algorithm for first-order gradient-based optimization
of stochastic objective functions, based on adaptive estimates of lower-order
moments. The method is straightforward to implement, is computationally
efficient, has little memory requirements, is invariant to diagonal rescaling
of the gradients... | computer science |
32,696 | Discriminative Clustering with Relative Constraints | cs.LG | We study the problem of clustering with relative constraints, where each
constraint specifies relative similarities among instances. In particular, each
constraint $(x_i, x_j, x_k)$ is acquired by posing a query: is instance $x_i$
more similar to $x_j$ than to $x_k$? We consider the scenario where answers to
such queri... | computer science |
32,697 | Comprehend DeepWalk as Matrix Factorization | cs.LG | Word2vec, as an efficient tool for learning vector representation of words
has shown its effectiveness in many natural language processing tasks. Mikolov
et al. issued Skip-Gram and Negative Sampling model for developing this
toolbox. Perozzi et al. introduced the Skip-Gram model into the study of social
network for th... | computer science |
32,698 | On Enhancing The Performance Of Nearest Neighbour Classifiers Using
Hassanat Distance Metric | cs.LG | We showed in this work how the Hassanat distance metric enhances the
performance of the nearest neighbour classifiers. The results demonstrate the
superiority of this distance metric over the traditional and most-used
distances, such as Manhattan distance and Euclidian distance. Moreover, we
proved that the Hassanat di... | computer science |
32,699 | Differential Search Algorithm-based Parametric Optimization of Fuzzy
Generalized Eigenvalue Proximal Support Vector Machine | cs.LG | Support Vector Machine (SVM) is an effective model for many classification
problems. However, SVM needs the solution of a quadratic program which require
specialized code. In addition, SVM has many parameters, which affects the
performance of SVM classifier. Recently, the Generalized Eigenvalue Proximal
SVM (GEPSVM) ha... | computer science |
32,700 | Efficient Online Relative Comparison Kernel Learning | cs.LG | Learning a kernel matrix from relative comparison human feedback is an
important problem with applications in collaborative filtering, object
retrieval, and search. For learning a kernel over a large number of objects,
existing methods face significant scalability issues inhibiting the application
of these methods to s... | computer science |
32,701 | Deep Autoencoders for Dimensionality Reduction of High-Content Screening
Data | cs.LG | High-content screening uses large collections of unlabeled cell image data to
reason about genetics or cell biology. Two important tasks are to identify
those cells which bear interesting phenotypes, and to identify sub-populations
enriched for these phenotypes. This exploratory data analysis usually involves
dimension... | computer science |
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