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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10,301 | Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative
Attribute Graphs | stat.ML | We describe the first sub-quadratic sampling algorithm for the Multiplicative
Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close
connection between MAGM and the Kronecker Product Graph Model (KPGM) of
Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices
to sample sm... | computer science |
10,302 | The multi-armed bandit problem with covariates | math.ST | We consider a multi-armed bandit problem in a setting where each arm produces
a noisy reward realization which depends on an observable random covariate. As
opposed to the traditional static multi-armed bandit problem, this setting
allows for dynamically changing rewards that better describe applications where
side inf... | computer science |
10,303 | PAC-Bayesian Inequalities for Martingales | cs.LG | We present a set of high-probability inequalities that control the
concentration of weighted averages of multiple (possibly uncountably many)
simultaneously evolving and interdependent martingales. Our results extend the
PAC-Bayesian analysis in learning theory from the i.i.d. setting to martingales
opening the way for... | computer science |
10,304 | Stochastic gradient descent on Riemannian manifolds | math.OC | Stochastic gradient descent is a simple approach to find the local minima of
a cost function whose evaluations are corrupted by noise. In this paper, we
develop a procedure extending stochastic gradient descent algorithms to the
case where the function is defined on a Riemannian manifold. We prove that, as
in the Eucli... | computer science |
10,305 | Falsification and future performance | stat.ML | We information-theoretically reformulate two measures of capacity from
statistical learning theory: empirical VC-entropy and empirical Rademacher
complexity. We show these capacity measures count the number of hypotheses
about a dataset that a learning algorithm falsifies when it finds the
classifier in its repertoire ... | computer science |
10,306 | Sparse Nonparametric Graphical Models | stat.ML | We present some nonparametric methods for graphical modeling. In the discrete
case, where the data are binary or drawn from a finite alphabet, Markov random
fields are already essentially nonparametric, since the cliques can take only a
finite number of values. Continuous data are different. The Gaussian graphical
mode... | computer science |
10,307 | Feature Selection via Regularized Trees | cs.LG | We propose a tree regularization framework, which enables many tree models to
perform feature selection efficiently. The key idea of the regularization
framework is to penalize selecting a new feature for splitting when its gain
(e.g. information gain) is similar to the features used in previous splits. The
regularizat... | computer science |
10,308 | Adaptive Policies for Sequential Sampling under Incomplete Information
and a Cost Constraint | stat.ML | We consider the problem of sequential sampling from a finite number of
independent statistical populations to maximize the expected infinite horizon
average outcome per period, under a constraint that the expected average
sampling cost does not exceed an upper bound. The outcome distributions are not
known. We construc... | computer science |
10,309 | Random Feature Maps for Dot Product Kernels | cs.LG | Approximating non-linear kernels using feature maps has gained a lot of
interest in recent years due to applications in reducing training and testing
times of SVM classifiers and other kernel based learning algorithms. We extend
this line of work and present low distortion embeddings for dot product kernels
into linear... | computer science |
10,310 | Change-Point Detection in Time-Series Data by Relative Density-Ratio
Estimation | stat.ML | The objective of change-point detection is to discover abrupt property
changes lying behind time-series data. In this paper, we present a novel
statistical change-point detection algorithm based on non-parametric divergence
estimation between time-series samples from two retrospective segments. Our
method uses the rela... | computer science |
10,311 | Infinite Shift-invariant Grouped Multi-task Learning for Gaussian
Processes | cs.LG | Multi-task learning leverages shared information among data sets to improve
the learning performance of individual tasks. The paper applies this framework
for data where each task is a phase-shifted periodic time series. In
particular, we develop a novel Bayesian nonparametric model capturing a mixture
of Gaussian proc... | computer science |
10,312 | Decentralized, Adaptive, Look-Ahead Particle Filtering | stat.ML | The decentralized particle filter (DPF) was proposed recently to increase the
level of parallelism of particle filtering. Given a decomposition of the state
space into two nested sets of variables, the DPF uses a particle filter to
sample the first set and then conditions on this sample to generate a set of
samples for... | computer science |
10,313 | Deviation optimal learning using greedy Q-aggregation | math.ST | Given a finite family of functions, the goal of model selection aggregation
is to construct a procedure that mimics the function from this family that is
the closest to an unknown regression function. More precisely, we consider a
general regression model with fixed design and measure the distance between
functions by ... | computer science |
10,314 | Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering
Birth/Death and Evolution of Topics in Text Stream | cs.IR | Topic models have proven to be a useful tool for discovering latent
structures in document collections. However, most document collections often
come as temporal streams and thus several aspects of the latent structure such
as the number of topics, the topics' distribution and popularity are
time-evolving. Several mode... | computer science |
10,315 | Algorithms and Complexity Results for Exact Bayesian Structure Learning | cs.LG | Bayesian structure learning is the NP-hard problem of discovering a Bayesian
network that optimally represents a given set of training data. In this paper
we study the computational worst-case complexity of exact Bayesian structure
learning under graph theoretic restrictions on the super-structure. The
super-structure ... | computer science |
10,316 | On the Equivalence between Herding and Conditional Gradient Algorithms | cs.LG | We show that the herding procedure of Welling (2009) takes exactly the form
of a standard convex optimization algorithm--namely a conditional gradient
algorithm minimizing a quadratic moment discrepancy. This link enables us to
invoke convergence results from convex optimization and to consider faster
alternatives for ... | computer science |
10,317 | Relax and Localize: From Value to Algorithms | cs.LG | We show a principled way of deriving online learning algorithms from a
minimax analysis. Various upper bounds on the minimax value, previously thought
to be non-constructive, are shown to yield algorithms. This allows us to
seamlessly recover known methods and to derive new ones. Our framework also
captures such "unort... | computer science |
10,318 | Fast projections onto mixed-norm balls with applications | stat.ML | Joint sparsity offers powerful structural cues for feature selection,
especially for variables that are expected to demonstrate a "grouped" behavior.
Such behavior is commonly modeled via group-lasso, multitask lasso, and related
methods where feature selection is effected via mixed-norms. Several mixed-norm
based spar... | computer science |
10,319 | Density-sensitive semisupervised inference | math.ST | Semisupervised methods are techniques for using labeled data
$(X_1,Y_1),\ldots,(X_n,Y_n)$ together with unlabeled data $X_{n+1},\ldots,X_N$
to make predictions. These methods invoke some assumptions that link the
marginal distribution $P_X$ of X to the regression function f(x). For example,
it is common to assume that ... | computer science |
10,320 | The asymptotics of ranking algorithms | math.ST | We consider the predictive problem of supervised ranking, where the task is
to rank sets of candidate items returned in response to queries. Although there
exist statistical procedures that come with guarantees of consistency in this
setting, these procedures require that individuals provide a complete ranking
of all i... | computer science |
10,321 | On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and
Applications | cs.LG | The role of kernels is central to machine learning. Motivated by the
importance of power-law distributions in statistical modeling, in this paper,
we propose the notion of power-law kernels to investigate power-laws in
learning problem. We propose two power-law kernels by generalizing Gaussian and
Laplacian kernels. Th... | computer science |
10,322 | A Simple Explanation of A Spectral Algorithm for Learning Hidden Markov
Models | stat.ME | A simple linear algebraic explanation of the algorithm in "A Spectral
Algorithm for Learning Hidden Markov Models" (COLT 2009). Most of the content
is in Figure 2; the text just makes everything precise in four nearly-trivial
claims. | computer science |
10,323 | Modeling Relational Data via Latent Factor Blockmodel | cs.DS | In this paper we address the problem of modeling relational data, which
appear in many applications such as social network analysis, recommender
systems and bioinformatics. Previous studies either consider latent feature
based models but disregarding local structure in the network, or focus
exclusively on capturing loc... | computer science |
10,324 | Probabilistic Latent Tensor Factorization Model for Link Pattern
Prediction in Multi-relational Networks | cs.SI | This paper aims at the problem of link pattern prediction in collections of
objects connected by multiple relation types, where each type may play a
distinct role. While common link analysis models are limited to single-type
link prediction, we attempt here to capture the correlations among different
relation types and... | computer science |
10,325 | EigenGP: Sparse Gaussian process models with data-dependent
eigenfunctions | cs.LG | Gaussian processes (GPs) provide a nonparametric representation of functions.
However, classical GP inference suffers from high computational cost and it is
difficult to design nonstationary GP priors in practice. In this paper, we
propose a sparse Gaussian process model, EigenGP, based on the Karhunen-Loeve
(KL) expan... | computer science |
10,326 | Message passing with relaxed moment matching | cs.LG | Bayesian learning is often hampered by large computational expense. As a
powerful generalization of popular belief propagation, expectation propagation
(EP) efficiently approximates the exact Bayesian computation. Nevertheless, EP
can be sensitive to outliers and suffer from divergence for difficult cases. To
address t... | computer science |
10,327 | Supervised Feature Selection in Graphs with Path Coding Penalties and
Network Flows | stat.ML | We consider supervised learning problems where the features are embedded in a
graph, such as gene expressions in a gene network. In this context, it is of
much interest to automatically select a subgraph with few connected components;
by exploiting prior knowledge, one can indeed improve the prediction
performance or o... | computer science |
10,328 | Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix
Factorization | stat.ML | In this paper, we study the nonnegative matrix factorization problem under
the separability assumption (that is, there exists a cone spanned by a small
subset of the columns of the input nonnegative data matrix containing all
columns), which is equivalent to the hyperspectral unmixing problem under the
linear mixing mo... | computer science |
10,329 | Nonparametric sparsity and regularization | stat.ML | In this work we are interested in the problems of supervised learning and
variable selection when the input-output dependence is described by a nonlinear
function depending on a few variables. Our goal is to consider a sparse
nonparametric model, hence avoiding linear or additive models. The key idea is
to measure the ... | computer science |
10,330 | Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertility | cs.LG | Data mining involves the systematic analysis of large data sets, and data
mining in agricultural soil datasets is exciting and modern research area. The
productive capacity of a soil depends on soil fertility. Achieving and
maintaining appropriate levels of soil fertility, is of utmost importance if
agricultural land i... | computer science |
10,331 | An Improved Bound for the Nystrom Method for Large Eigengap | cs.LG | We develop an improved bound for the approximation error of the Nystr\"{o}m
method under the assumption that there is a large eigengap in the spectrum of
kernel matrix. This is based on the empirical observation that the eigengap has
a significant impact on the approximation error of the Nystr\"{o}m method. Our
approac... | computer science |
10,332 | A History of Cluster Analysis Using the Classification Society's
Bibliography Over Four Decades | cs.DL | The Classification Literature Automated Search Service, an annual
bibliography based on citation of one or more of a set of around 80 book or
journal publications, ran from 1972 to 2012. We analyze here the years 1994 to
2011. The Classification Society's Service, as it was termed, has been produced
by the Classificati... | computer science |
10,333 | Proximal methods for the latent group lasso penalty | math.OC | We consider a regularized least squares problem, with regularization by
structured sparsity-inducing norms, which extend the usual $\ell_1$ and the
group lasso penalty, by allowing the subsets to overlap. Such regularizations
lead to nonsmooth problems that are difficult to optimize, and we propose in
this paper a suit... | computer science |
10,334 | Learning Model-Based Sparsity via Projected Gradient Descent | stat.ML | Several convex formulation methods have been proposed previously for
statistical estimation with structured sparsity as the prior. These methods
often require a carefully tuned regularization parameter, often a cumbersome or
heuristic exercise. Furthermore, the estimate that these methods produce might
not belong to th... | computer science |
10,335 | Stochastic Dual Coordinate Ascent Methods for Regularized Loss
Minimization | stat.ML | Stochastic Gradient Descent (SGD) has become popular for solving large scale
supervised machine learning optimization problems such as SVM, due to their
strong theoretical guarantees. While the closely related Dual Coordinate Ascent
(DCA) method has been implemented in various software packages, it has so far
lacked go... | computer science |
10,336 | On the Complexity of Bandit and Derivative-Free Stochastic Convex
Optimization | cs.LG | The problem of stochastic convex optimization with bandit feedback (in the
learning community) or without knowledge of gradients (in the optimization
community) has received much attention in recent years, in the form of
algorithms and performance upper bounds. However, much less is known about the
inherent complexity ... | computer science |
10,337 | Regret Bounds for Restless Markov Bandits | cs.LG | We consider the restless Markov bandit problem, in which the state of each
arm evolves according to a Markov process independently of the learner's
actions. We suggest an algorithm that after $T$ steps achieves
$\tilde{O}(\sqrt{T})$ regret with respect to the best policy that knows the
distributions of all arms. No ass... | computer science |
10,338 | Thompson Sampling for Contextual Bandits with Linear Payoffs | cs.LG | Thompson Sampling is one of the oldest heuristics for multi-armed bandit
problems. It is a randomized algorithm based on Bayesian ideas, and has
recently generated significant interest after several studies demonstrated it
to have better empirical performance compared to the state-of-the-art methods.
However, many ques... | computer science |
10,339 | Further Optimal Regret Bounds for Thompson Sampling | cs.LG | Thompson Sampling is one of the oldest heuristics for multi-armed bandit
problems. It is a randomized algorithm based on Bayesian ideas, and has
recently generated significant interest after several studies demonstrated it
to have better empirical performance compared to the state of the art methods.
In this paper, we ... | computer science |
10,340 | Transferring Subspaces Between Subjects in Brain-Computer Interfacing | stat.ML | Compensating changes between a subjects' training and testing session in
Brain Computer Interfacing (BCI) is challenging but of great importance for a
robust BCI operation. We show that such changes are very similar between
subjects, thus can be reliably estimated using data from other users and
utilized to construct a... | computer science |
10,341 | Comunication-Efficient Algorithms for Statistical Optimization | stat.ML | We analyze two communication-efficient algorithms for distributed statistical
optimization on large-scale data sets. The first algorithm is a standard
averaging method that distributes the $N$ data samples evenly to $\nummac$
machines, performs separate minimization on each subset, and then averages the
estimates. We p... | computer science |
10,342 | An efficient model-free estimation of multiclass conditional probability | stat.ML | Conventional multiclass conditional probability estimation methods, such as
Fisher's discriminate analysis and logistic regression, often require
restrictive distributional model assumption. In this paper, a model-free
estimation method is proposed to estimate multiclass conditional probability
through a series of cond... | computer science |
10,343 | Learning Topic Models and Latent Bayesian Networks Under Expansion
Constraints | stat.ML | Unsupervised estimation of latent variable models is a fundamental problem
central to numerous applications of machine learning and statistics. This work
presents a principled approach for estimating broad classes of such models,
including probabilistic topic models and latent linear Bayesian networks, using
only secon... | computer science |
10,344 | Towards a learning-theoretic analysis of spike-timing dependent
plasticity | cs.LG | This paper suggests a learning-theoretic perspective on how synaptic
plasticity benefits global brain functioning. We introduce a model, the
selectron, that (i) arises as the fast time constant limit of leaky
integrate-and-fire neurons equipped with spiking timing dependent plasticity
(STDP) and (ii) is amenable to the... | computer science |
10,345 | Supervised Blockmodelling | cs.LG | Collective classification models attempt to improve classification
performance by taking into account the class labels of related instances.
However, they tend not to learn patterns of interactions between classes and/or
make the assumption that instances of the same class link to each other
(assortativity assumption).... | computer science |
10,346 | Bayesian Mixture Models for Frequent Itemset Discovery | cs.LG | In binary-transaction data-mining, traditional frequent itemset mining often
produces results which are not straightforward to interpret. To overcome this
problem, probability models are often used to produce more compact and
conclusive results, albeit with some loss of accuracy. Bayesian statistics have
been widely us... | computer science |
10,347 | The Issue-Adjusted Ideal Point Model | stat.ML | We develop a model of issue-specific voting behavior. This model can be used
to explore lawmakers' personal voting patterns of voting by issue area,
providing an exploratory window into how the language of the law is correlated
with political support. We derive approximate posterior inference algorithms
based on variat... | computer science |
10,348 | Partial Gaussian Graphical Model Estimation | cs.LG | This paper studies the partial estimation of Gaussian graphical models from
high-dimensional empirical observations. We derive a convex formulation for
this problem using $\ell_1$-regularized maximum-likelihood estimation, which
can be solved via a block coordinate descent algorithm. Statistical estimation
performance ... | computer science |
10,349 | Learning Hierarchical Object Maps Of Non-Stationary Environments with
mobile robots | cs.LG | Building models, or maps, of robot environments is a highly active research
area; however, most existing techniques construct unstructured maps and assume
static environments. In this paper, we present an algorithm for learning object
models of non-stationary objects found in office-type environments. Our
algorithm exp... | computer science |
10,350 | Learning with Scope, with Application to Information Extraction and
Classification | cs.LG | In probabilistic approaches to classification and information extraction, one
typically builds a statistical model of words under the assumption that future
data will exhibit the same regularities as the training data. In many data
sets, however, there are scope-limited features whose predictive power is only
applicabl... | computer science |
10,351 | Optimal Time Bounds for Approximate Clustering | cs.DS | Clustering is a fundamental problem in unsupervised learning, and has been
studied widely both as a problem of learning mixture models and as an
optimization problem. In this paper, we study clustering with respect the
emph{k-median} objective function, a natural formulation of clustering in which
we attempt to minimiz... | computer science |
10,352 | Expectation-Propogation for the Generative Aspect Model | cs.LG | The generative aspect model is an extension of the multinomial model for text
that allows word probabilities to vary stochastically across documents.
Previous results with aspect models have been promising, but hindered by the
computational difficulty of carrying out inference and learning. This paper
demonstrates that... | computer science |
10,353 | Finding the True Frequent Itemsets | cs.LG | Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It
requires to identify all itemsets appearing in at least a fraction $\theta$ of
a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of
mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the
understandin... | computer science |
10,354 | Network-based clustering with mixtures of L1-penalized Gaussian
graphical models: an empirical investigation | stat.ML | In many applications, multivariate samples may harbor previously unrecognized
heterogeneity at the level of conditional independence or network structure.
For example, in cancer biology, disease subtypes may differ with respect to
subtype-specific interplay between molecular components. Then, both subtype
discovery and... | computer science |
10,355 | Variational MCMC | cs.LG | We propose a new class of learning algorithms that combines variational
approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms
that use the variational approximation as proposal distribution can perform
poorly because this approximation tends to underestimate the true variance and
other features... | computer science |
10,356 | Probabilistic Models for Unified Collaborative and Content-Based
Recommendation in Sparse-Data Environments | cs.IR | Recommender systems leverage product and community information to target
products to consumers. Researchers have developed collaborative recommenders,
content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a
unified probabilistic framework for merging collaborative and content-based
recommendations... | computer science |
10,357 | A Triclustering Approach for Time Evolving Graphs | cs.LG | This paper introduces a novel technique to track structures in time evolving
graphs. The method is based on a parameter free approach for three-dimensional
co-clustering of the source vertices, the target vertices and the time. All
these features are simultaneously segmented in order to build time segments and
clusters... | computer science |
10,358 | Multi-agent learning using Fictitious Play and Extended Kalman Filter | cs.MA | Decentralised optimisation tasks are important components of multi-agent
systems. These tasks can be interpreted as n-player potential games: therefore
game-theoretic learning algorithms can be used to solve decentralised
optimisation tasks. Fictitious play is the canonical example of these
algorithms. Nevertheless fic... | computer science |
10,359 | An Efficient Sufficient Dimension Reduction Method for Identifying
Genetic Variants of Clinical Significance | cs.LG | Fast and cheaper next generation sequencing technologies will generate
unprecedentedly massive and highly-dimensional genomic and epigenomic variation
data. In the near future, a routine part of medical record will include the
sequenced genomes. A fundamental question is how to efficiently extract genomic
and epigenomi... | computer science |
10,360 | The Anchors Hierachy: Using the triangle inequality to survive high
dimensional data | cs.LG | This paper is about metric data structures in high-dimensional or
non-Euclidean space that permit cached sufficient statistics accelerations of
learning algorithms.
It has recently been shown that for less than about 10 dimensions, decorating
kd-trees with additional "cached sufficient statistics" such as first and
s... | computer science |
10,361 | Monte Carlo Inference via Greedy Importance Sampling | cs.LG | We present a new method for conducting Monte Carlo inference in graphical
models which combines explicit search with generalized importance sampling. The
idea is to reduce the variance of importance sampling by searching for
significant points in the target distribution. We prove that it is possible to
introduce search... | computer science |
10,362 | Herded Gibbs Sampling | cs.LG | The Gibbs sampler is one of the most popular algorithms for inference in
statistical models. In this paper, we introduce a herding variant of this
algorithm, called herded Gibbs, that is entirely deterministic. We prove that
herded Gibbs has an $O(1/T)$ convergence rate for models with independent
variables and for ful... | computer science |
10,363 | Affinity Weighted Embedding | cs.IR | Supervised (linear) embedding models like Wsabie and PSI have proven
successful at ranking, recommendation and annotation tasks. However, despite
being scalable to large datasets they do not take full advantage of the extra
data due to their linear nature, and typically underfit. We propose a new class
of models which ... | computer science |
10,364 | A Linearly Convergent Conditional Gradient Algorithm with Applications
to Online and Stochastic Optimization | cs.LG | Linear optimization is many times algorithmically simpler than non-linear
convex optimization. Linear optimization over matroid polytopes, matching
polytopes and path polytopes are example of problems for which we have simple
and efficient combinatorial algorithms, but whose non-linear convex counterpart
is harder and ... | computer science |
10,365 | Cellular Tree Classifiers | stat.ML | The cellular tree classifier model addresses a fundamental problem in the
design of classifiers for a parallel or distributed computing world: Given a
data set, is it sufficient to apply a majority rule for classification, or
shall one split the data into two or more parts and send each part to a
potentially different ... | computer science |
10,366 | A Linear Time Active Learning Algorithm for Link Classification -- Full
Version -- | cs.LG | We present very efficient active learning algorithms for link classification
in signed networks. Our algorithms are motivated by a stochastic model in which
edge labels are obtained through perturbations of a initial sign assignment
consistent with a two-clustering of the nodes. We provide a theoretical
analysis within... | computer science |
10,367 | A Correlation Clustering Approach to Link Classification in Signed
Networks -- Full Version -- | cs.LG | Motivated by social balance theory, we develop a theory of link
classification in signed networks using the correlation clustering index as
measure of label regularity. We derive learning bounds in terms of correlation
clustering within three fundamental transductive learning settings: online,
batch and active. Our mai... | computer science |
10,368 | Dirichlet draws are sparse with high probability | cs.LG | This note provides an elementary proof of the folklore fact that draws from a
Dirichlet distribution (with parameters less than 1) are typically sparse (most
coordinates are small). | computer science |
10,369 | Evaluation of a Supervised Learning Approach for Stock Market Operations | stat.ML | Data mining methods have been widely applied in financial markets, with the
purpose of providing suitable tools for prices forecasting and automatic
trading. Particularly, learning methods aim to identify patterns in time series
and, based on such patterns, to recommend buy/sell operations. The objective of
this work i... | computer science |
10,370 | The connection between Bayesian estimation of a Gaussian random field
and RKHS | stat.ML | Reconstruction of a function from noisy data is often formulated as a
regularized optimization problem over an infinite-dimensional reproducing
kernel Hilbert space (RKHS). The solution describes the observed data and has a
small RKHS norm. When the data fit is measured using a quadratic loss, this
estimator has a know... | computer science |
10,371 | Equitability Analysis of the Maximal Information Coefficient, with
Comparisons | cs.LG | A measure of dependence is said to be equitable if it gives similar scores to
equally noisy relationships of different types. Equitability is important in
data exploration when the goal is to identify a relatively small set of
strongest associations within a dataset as opposed to finding as many non-zero
associations a... | computer science |
10,372 | Discriminative Feature Selection for Uncertain Graph Classification | cs.LG | Mining discriminative features for graph data has attracted much attention in
recent years due to its important role in constructing graph classifiers,
generating graph indices, etc. Most measurement of interestingness of
discriminative subgraph features are defined on certain graphs, where the
structure of graph objec... | computer science |
10,373 | Probabilistic Latent Semantic Analysis | cs.LG | Probabilistic Latent Semantic Analysis is a novel statistical technique for
the analysis of two-mode and co-occurrence data, which has applications in
information retrieval and filtering, natural language processing, machine
learning from text, and in related areas. Compared to standard Latent Semantic
Analysis which s... | computer science |
10,374 | An alternative text representation to TF-IDF and Bag-of-Words | cs.IR | In text mining, information retrieval, and machine learning, text documents
are commonly represented through variants of sparse Bag of Words (sBoW) vectors
(e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer
from their inherent over-sparsity and fail to capture word-level synonymy and
polyse... | computer science |
10,375 | Link prediction for partially observed networks | stat.ML | Link prediction is one of the fundamental problems in network analysis. In
many applications, notably in genetics, a partially observed network may not
contain any negative examples of absent edges, which creates a difficulty for
many existing supervised learning approaches. We develop a new method which
treats the obs... | computer science |
10,376 | Rank regularization and Bayesian inference for tensor completion and
extrapolation | cs.IT | A novel regularizer of the PARAFAC decomposition factors capturing the
tensor's rank is proposed in this paper, as the key enabler for completion of
three-way data arrays with missing entries. Set in a Bayesian framework, the
tensor completion method incorporates prior information to enhance its
smoothing and predictio... | computer science |
10,377 | Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks | cs.LG | This paper considers networks where relationships between nodes are
represented by directed dissimilarities. The goal is to study methods for the
determination of hierarchical clusters, i.e., a family of nested partitions
indexed by a connectivity parameter, induced by the given dissimilarity
structures. Our constructi... | computer science |
10,378 | ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process
Mixtures | stat.ML | The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian
nonparametric modeling, and is widely used in tasks such as density estimation,
natural language processing, and time series modeling. Although MCMC inference
methods for the DP often provide a gold standard in terms asymptotic accuracy,
they ca... | computer science |
10,379 | The PAV algorithm optimizes binary proper scoring rules | stat.AP | There has been much recent interest in application of the
pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the
probabilistic outputs of automatic pattern recognition and machine learning
algorithms. Special cost functions, known as proper scoring rules form natural
objective functions to judge the... | computer science |
10,380 | The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New
DCF | stat.AP | The change of two orders of magnitude in the 'new DCF' of NIST's SRE'10,
relative to the 'old DCF' evaluation criterion, posed a difficult challenge for
participants and evaluator alike. Initially, participants were at a loss as to
how to calibrate their systems, while the evaluator underestimated the required
number o... | computer science |
10,381 | Distributed dictionary learning over a sensor network | stat.ML | We consider the problem of distributed dictionary learning, where a set of
nodes is required to collectively learn a common dictionary from noisy
measurements. This approach may be useful in several contexts including sensor
networks. Diffusion cooperation schemes have been proposed to solve the
distributed linear regr... | computer science |
10,382 | Multi-Label Classifier Chains for Bird Sound | cs.LG | Bird sound data collected with unattended microphones for automatic surveys,
or mobile devices for citizen science, typically contain multiple
simultaneously vocalizing birds of different species. However, few works have
considered the multi-label structure in birdsong. We propose to use an ensemble
of classifier chain... | computer science |
10,383 | The K-modes algorithm for clustering | cs.LG | Many clustering algorithms exist that estimate a cluster centroid, such as
K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters
data by returning exactly K meaningful modes. We propose a natural definition
of a K-modes objective function by combining the notions of density and cluster
assignm... | computer science |
10,384 | A Theoretical Analysis of NDCG Type Ranking Measures | cs.LG | A central problem in ranking is to design a ranking measure for evaluation of
ranking functions. In this paper we study, from a theoretical perspective, the
widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures.
Although there are extensive empirical studies of NDCG, little is known about
its t... | computer science |
10,385 | Low-rank optimization for distance matrix completion | math.OC | This paper addresses the problem of low-rank distance matrix completion. This
problem amounts to recover the missing entries of a distance matrix when the
dimension of the data embedding space is possibly unknown but small compared to
the number of considered data points. The focus is on high-dimensional
problems. We r... | computer science |
10,386 | Supervised Heterogeneous Multiview Learning for Joint Association Study
and Disease Diagnosis | cs.LG | Given genetic variations and various phenotypical traits, such as Magnetic
Resonance Imaging (MRI) features, we consider two important and related tasks
in biomedical research: i)to select genetic and phenotypical markers for
disease diagnosis and ii) to identify associations between genetic and
phenotypical data. Thes... | computer science |
10,387 | Semi-supervised Eigenvectors for Large-scale Locally-biased Learning | cs.LG | In many applications, one has side information, e.g., labels that are
provided in a semi-supervised manner, about a specific target region of a large
data set, and one wants to perform machine learning and data analysis tasks
"nearby" that prespecified target region. For example, one might be interested
in the clusteri... | computer science |
10,388 | Optimal amortized regret in every interval | cs.LG | Consider the classical problem of predicting the next bit in a sequence of
bits. A standard performance measure is {\em regret} (loss in payoff) with
respect to a set of experts. For example if we measure performance with respect
to two constant experts one that always predicts 0's and another that always
predicts 1's ... | computer science |
10,389 | Local Graph Clustering Beyond Cheeger's Inequality | cs.DS | Motivated by applications of large-scale graph clustering, we study
random-walk-based LOCAL algorithms whose running times depend only on the size
of the output cluster, rather than the entire graph. All previously known such
algorithms guarantee an output conductance of $\tilde{O}(\sqrt{\phi(A)})$ when
the target set ... | computer science |
10,390 | Phase Retrieval using Alternating Minimization | stat.ML | Phase retrieval problems involve solving linear equations, but with missing
sign (or phase, for complex numbers) information. More than four decades after
it was first proposed, the seminal error reduction algorithm of (Gerchberg and
Saxton 1972) and (Fienup 1982) is still the popular choice for solving many
variants o... | computer science |
10,391 | Probabilistic Solutions to Differential Equations and their Application
to Riemannian Statistics | stat.ML | We study a probabilistic numerical method for the solution of both boundary
and initial value problems that returns a joint Gaussian process posterior over
the solution. Such methods have concrete value in the statistics on Riemannian
manifolds, where non-analytic ordinary differential equations are involved in
virtual... | computer science |
10,392 | Distributed k-Means and k-Median Clustering on General Topologies | cs.LG | This paper provides new algorithms for distributed clustering for two popular
center-based objectives, k-median and k-means. These algorithms have provable
guarantees and improve communication complexity over existing approaches.
Following a classic approach in clustering by \cite{har2004coresets}, we reduce
the proble... | computer science |
10,393 | Provable Inductive Matrix Completion | cs.LG | Consider a movie recommendation system where apart from the ratings
information, side information such as user's age or movie's genre is also
available. Unlike standard matrix completion, in this setting one should be
able to predict inductively on new users/movies. In this paper, we study the
problem of inductive matr... | computer science |
10,394 | A Gang of Bandits | cs.LG | Multi-armed bandit problems are receiving a great deal of attention because
they adequately formalize the exploration-exploitation trade-offs arising in
several industrially relevant applications, such as online advertisement and,
more generally, recommendation systems. In many cases, however, these
applications have a... | computer science |
10,395 | Kernel Mean Estimation and Stein's Effect | stat.ML | A mean function in reproducing kernel Hilbert space, or a kernel mean, is an
important part of many applications ranging from kernel principal component
analysis to Hilbert-space embedding of distributions. Given finite samples, an
empirical average is the standard estimate for the true kernel mean. We show
that this e... | computer science |
10,396 | Multiclass Total Variation Clustering | stat.ML | Ideas from the image processing literature have recently motivated a new set
of clustering algorithms that rely on the concept of total variation. While
these algorithms perform well for bi-partitioning tasks, their recursive
extensions yield unimpressive results for multiclass clustering tasks. This
paper presents a g... | computer science |
10,397 | Verdict Accuracy of Quick Reduct Algorithm using Clustering and
Classification Techniques for Gene Expression Data | cs.LG | In most gene expression data, the number of training samples is very small
compared to the large number of genes involved in the experiments. However,
among the large amount of genes, only a small fraction is effective for
performing a certain task. Furthermore, a small subset of genes is desirable in
developing gene e... | computer science |
10,398 | Diffusion map for clustering fMRI spatial maps extracted by independent
component analysis | cs.CE | Functional magnetic resonance imaging (fMRI) produces data about activity
inside the brain, from which spatial maps can be extracted by independent
component analysis (ICA). In datasets, there are n spatial maps that contain p
voxels. The number of voxels is very high compared to the number of analyzed
spatial maps. Cl... | computer science |
10,399 | Fast greedy algorithm for subspace clustering from corrupted and
incomplete data | cs.LG | We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm
providing an efficient method for clustering data belonging to a few
low-dimensional linear or affine subspaces. The main difference of our
algorithm from predecessors is its ability to work with noisy data having a
high rate of erasures (missed e... | computer science |
10,400 | Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean
Separation | stat.ML | While several papers have investigated computationally and statistically
efficient methods for learning Gaussian mixtures, precise minimax bounds for
their statistical performance as well as fundamental limits in high-dimensional
settings are not well-understood. In this paper, we provide precise information
theoretic ... | computer science |
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