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10,501 | Hypothesis testing using pairwise distances and associated kernels (with
Appendix) | cs.LG | We provide a unifying framework linking two classes of statistics used in
two-sample and independence testing: on the one hand, the energy distances and
distance covariances from the statistics literature; on the other, distances
between embeddings of distributions to reproducing kernel Hilbert spaces
(RKHS), as establ... | computer science |
10,502 | Sparse group lasso and high dimensional multinomial classification | stat.ML | The sparse group lasso optimization problem is solved using a coordinate
gradient descent algorithm. The algorithm is applicable to a broad class of
convex loss functions. Convergence of the algorithm is established, and the
algorithm is used to investigate the performance of the multinomial sparse
group lasso classifi... | computer science |
10,503 | Compressed Sensing for Energy-Efficient Wireless Telemonitoring of
Noninvasive Fetal ECG via Block Sparse Bayesian Learning | stat.ML | Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The
design of a telemonitoring system via a wireless body-area network with low
energy consumption for ambulatory use is highly desirable. As an emerging
technique, compressed sensing (CS) shows great promise in
compressing/reconstructing data with... | computer science |
10,504 | Dynamic Behavioral Mixed-Membership Model for Large Evolving Networks | cs.SI | The majority of real-world networks are dynamic and extremely large (e.g.,
Internet Traffic, Twitter, Facebook, ...). To understand the structural
behavior of nodes in these large dynamic networks, it may be necessary to model
the dynamics of behavioral roles representing the main connectivity patterns
over time. In th... | computer science |
10,505 | Modularity-Based Clustering for Network-Constrained Trajectories | stat.ML | We present a novel clustering approach for moving object trajectories that
are constrained by an underlying road network. The approach builds a similarity
graph based on these trajectories then uses modularity-optimization hiearchical
graph clustering to regroup trajectories with similar profiles. Our
experimental stud... | computer science |
10,506 | A Discussion on Parallelization Schemes for Stochastic Vector
Quantization Algorithms | stat.ML | This paper studies parallelization schemes for stochastic Vector Quantization
algorithms in order to obtain time speed-ups using distributed resources. We
show that the most intuitive parallelization scheme does not lead to better
performances than the sequential algorithm. Another distributed scheme is
therefore intro... | computer science |
10,507 | Sparse Approximation via Penalty Decomposition Methods | cs.LG | In this paper we consider sparse approximation problems, that is, general
$l_0$ minimization problems with the $l_0$-"norm" of a vector being a part of
constraints or objective function. In particular, we first study the
first-order optimality conditions for these problems. We then propose penalty
decomposition (PD) me... | computer science |
10,508 | Modeling Discrete Interventional Data using Directed Cyclic Graphical
Models | stat.ML | We outline a representation for discrete multivariate distributions in terms
of interventional potential functions that are globally normalized. This
representation can be used to model the effects of interventions, and the
independence properties encoded in this model can be represented as a directed
graph that allows... | computer science |
10,509 | BPR: Bayesian Personalized Ranking from Implicit Feedback | cs.IR | Item recommendation is the task of predicting a personalized ranking on a set
of items (e.g. websites, movies, products). In this paper, we investigate the
most common scenario with implicit feedback (e.g. clicks, purchases). There are
many methods for item recommendation from implicit feedback like matrix
factorizatio... | computer science |
10,510 | Using the Gene Ontology Hierarchy when Predicting Gene Function | cs.LG | The problem of multilabel classification when the labels are related through
a hierarchical categorization scheme occurs in many application domains such as
computational biology. For example, this problem arises naturally when trying
to automatically assign gene function using a controlled vocabularies like Gene
Ontol... | computer science |
10,511 | Improving Compressed Counting | cs.DS | Compressed Counting (CC) [22] was recently proposed for estimating the ath
frequency moments of data streams, where 0 < a <= 2. CC can be used for
estimating Shannon entropy, which can be approximated by certain functions of
the ath frequency moments as a -> 1. Monitoring Shannon entropy for anomaly
detection (e.g., DD... | computer science |
10,512 | Bayesian Discovery of Linear Acyclic Causal Models | stat.ML | Methods for automated discovery of causal relationships from
non-interventional data have received much attention recently. A widely used
and well understood model family is given by linear acyclic causal models
(recursive structural equation models). For Gaussian data both constraint-based
methods (Spirtes et al., 199... | computer science |
10,513 | Learning Continuous-Time Social Network Dynamics | cs.SI | We demonstrate that a number of sociology models for social network dynamics
can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based
approximate inference method for CTBNs can be used as the basis of an
expectation-maximization procedure that achieves better accuracy in estimating
the parameters of... | computer science |
10,514 | Convex Coding | cs.LG | Inspired by recent work on convex formulations of clustering (Lashkari &
Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the
Sparse Coding Problem (Olshausen & Field, 1997). In sparse coding we attempt to
simultaneously represent a sequence of data-vectors sparsely (i.e. sparse
approximation (... | computer science |
10,515 | Theory of Dependent Hierarchical Normalized Random Measures | cs.LG | This paper presents theory for Normalized Random Measures (NRMs), Normalized
Generalized Gammas (NGGs), a particular kind of NRM, and Dependent Hierarchical
NRMs which allow networks of dependent NRMs to be analysed. These have been
used, for instance, for time-dependent topic modelling. In this paper, we first
introdu... | computer science |
10,516 | Soft Rule Ensembles for Statistical Learning | stat.ML | In this article supervised learning problems are solved using soft rule
ensembles. We first review the importance sampling learning ensembles (ISLE)
approach that is useful for generating hard rules. The soft rules are then
obtained with logistic regression from the corresponding hard rules. In order
to deal with the p... | computer science |
10,517 | Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by
Exploiting Structure | cs.LG | In this work we consider the stochastic minimization of nonsmooth convex loss
functions, a central problem in machine learning. We propose a novel algorithm
called Accelerated Nonsmooth Stochastic Gradient Descent (ANSGD), which
exploits the structure of common nonsmooth loss functions to achieve optimal
convergence ra... | computer science |
10,518 | Towards a Mathematical Foundation of Immunology and Amino Acid Chains | stat.ML | We attempt to set a mathematical foundation of immunology and amino acid
chains. To measure the similarities of these chains, a kernel on strings is
defined using only the sequence of the chains and a good amino acid
substitution matrix (e.g. BLOSUM62). The kernel is used in learning machines to
predict binding affinit... | computer science |
10,519 | A Framework for Evaluating Approximation Methods for Gaussian Process
Regression | stat.ML | Gaussian process (GP) predictors are an important component of many Bayesian
approaches to machine learning. However, even a straightforward implementation
of Gaussian process regression (GPR) requires O(n^2) space and O(n^3) time for
a dataset of n examples. Several approximation methods have been proposed, but
there ... | computer science |
10,520 | Finding Important Genes from High-Dimensional Data: An Appraisal of
Statistical Tests and Machine-Learning Approaches | stat.ML | Over the past decades, statisticians and machine-learning researchers have
developed literally thousands of new tools for the reduction of
high-dimensional data in order to identify the variables most responsible for a
particular trait. These tools have applications in a plethora of settings,
including data analysis in... | computer science |
10,521 | Inferring land use from mobile phone activity | stat.ML | Understanding the spatiotemporal distribution of people within a city is
crucial to many planning applications. Obtaining data to create required
knowledge, currently involves costly survey methods. At the same time
ubiquitous mobile sensors from personal GPS devices to mobile phones are
collecting massive amounts of d... | computer science |
10,522 | Bayes Blocks: An Implementation of the Variational Bayesian Building
Blocks Framework | cs.MS | A software library for constructing and learning probabilistic models is
presented. The library offers a set of building blocks from which a large
variety of static and dynamic models can be built. These include hierarchical
models for variances of other variables and many nonlinear models. The
underlying variational B... | computer science |
10,523 | Toward Practical N2 Monte Carlo: the Marginal Particle Filter | stat.CO | Sequential Monte Carlo techniques are useful for state estimation in
non-linear, non-Gaussian dynamic models. These methods allow us to approximate
the joint posterior distribution using sequential importance sampling. In this
framework, the dimension of the target distribution grows with each time step,
thus it is nec... | computer science |
10,524 | A submodular-supermodular procedure with applications to discriminative
structure learning | cs.LG | In this paper, we present an algorithm for minimizing the difference between
two submodular functions using a variational framework which is based on (an
extension of) the concave-convex procedure [17]. Because several commonly used
metrics in machine learning, like mutual information and conditional mutual
information... | computer science |
10,525 | Discovery of non-gaussian linear causal models using ICA | cs.LG | In recent years, several methods have been proposed for the discovery of
causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000).
Such methods make various assumptions on the data generating process to
facilitate its identification from purely observational data. Continuing this
line of research, ... | computer science |
10,526 | Two-Way Latent Grouping Model for User Preference Prediction | cs.IR | We introduce a novel latent grouping model for predicting the relevance of a
new document to a user. The model assumes a latent group structure for both
users and documents. We compared the model against a state-of-the-art method,
the User Rating Profile model, where only users have a latent group structure.
We estimat... | computer science |
10,527 | Mining Associated Text and Images with Dual-Wing Harmoniums | cs.LG | We propose a multi-wing harmonium model for mining multimedia data that
extends and improves on earlier models based on two-layer random fields, which
capture bidirectional dependencies between hidden topic aspects and observed
inputs. This model can be viewed as an undirected counterpart of the two-layer
directed mode... | computer science |
10,528 | Forecasting electricity consumption by aggregating specialized experts | stat.ML | We consider the setting of sequential prediction of arbitrary sequences based
on specialized experts. We first provide a review of the relevant literature
and present two theoretical contributions: a general analysis of the specialist
aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules
of He... | computer science |
10,529 | Estimating a Causal Order among Groups of Variables in Linear Models | stat.ML | The machine learning community has recently devoted much attention to the
problem of inferring causal relationships from statistical data. Most of this
work has focused on uncovering connections among scalar random variables. We
generalize existing methods to apply to collections of multi-dimensional random
vectors, fo... | computer science |
10,530 | Comparative Study for Inference of Hidden Classes in Stochastic Block
Models | cs.LG | Inference of hidden classes in stochastic block model is a classical problem
with important applications. Most commonly used methods for this problem
involve na\"{\i}ve mean field approaches or heuristic spectral methods.
Recently, belief propagation was proposed for this problem. In this
contribution we perform a comp... | computer science |
10,531 | A Spectral Learning Approach to Range-Only SLAM | cs.LG | We present a novel spectral learning algorithm for simultaneous localization
and mapping (SLAM) from range data with known correspondences. This algorithm
is an instance of a general spectral system identification framework, from
which it inherits several desirable properties, including statistical
consistency and no l... | computer science |
10,532 | Near-Optimal Algorithms for Differentially-Private Principal Components | stat.ML | Principal components analysis (PCA) is a standard tool for identifying good
low-dimensional approximations to data in high dimension. Many data sets of
interest contain private or sensitive information about individuals. Algorithms
which operate on such data should be sensitive to the privacy risks in
publishing their ... | computer science |
10,533 | Expectation Propagation in Gaussian Process Dynamical Systems: Extended
Version | stat.ML | Rich and complex time-series data, such as those generated from engineering
systems, financial markets, videos or neural recordings, are now a common
feature of modern data analysis. Explaining the phenomena underlying these
diverse data sets requires flexible and accurate models. In this paper, we
promote Gaussian pro... | computer science |
10,534 | Distributed Strongly Convex Optimization | cs.DC | A lot of effort has been invested into characterizing the convergence rates
of gradient based algorithms for non-linear convex optimization. Recently,
motivated by large datasets and problems in machine learning, the interest has
shifted towards distributed optimization. In this work we present a distributed
algorithm ... | computer science |
10,535 | MahNMF: Manhattan Non-negative Matrix Factorization | stat.ML | Non-negative matrix factorization (NMF) approximates a non-negative matrix
$X$ by a product of two non-negative low-rank factor matrices $W$ and $H$. NMF
and its extensions minimize either the Kullback-Leibler divergence or the
Euclidean distance between $X$ and $W^T H$ to model the Poisson noise or the
Gaussian noise.... | computer science |
10,536 | Surrogate Losses in Passive and Active Learning | math.ST | Active learning is a type of sequential design for supervised machine
learning, in which the learning algorithm sequentially requests the labels of
selected instances from a large pool of unlabeled data points. The objective is
to produce a classifier of relatively low risk, as measured under the 0-1 loss,
ideally usin... | computer science |
10,537 | Iterative Conditional Fitting for Gaussian Ancestral Graph Models | stat.ME | Ancestral graph models, introduced by Richardson and Spirtes (2002),
generalize both Markov random fields and Bayesian networks to a class of graphs
with a global Markov property that is closed under conditioning and
marginalization. By design, ancestral graphs encode precisely the conditional
independence structures t... | computer science |
10,538 | A Bayesian Approach toward Active Learning for Collaborative Filtering | cs.LG | Collaborative filtering is a useful technique for exploiting the preference
patterns of a group of users to predict the utility of items for the active
user. In general, the performance of collaborative filtering depends on the
number of rated examples given by the active user. The more the number of rated
examples giv... | computer science |
10,539 | PAC-learning bounded tree-width Graphical Models | cs.LG | We show that the class of strongly connected graphical models with treewidth
at most k can be properly efficiently PAC-learnt with respect to the
Kullback-Leibler Divergence. Previous approaches to this problem, such as those
of Chow ([1]), and Ho gen ([7]) have shown that this class is PAC-learnable by
reducing it to ... | computer science |
10,540 | An Integrated, Conditional Model of Information Extraction and
Coreference with Applications to Citation Matching | cs.LG | Although information extraction and coreference resolution appear together in
many applications, most current systems perform them as ndependent steps. This
paper describes an approach to integrated inference for extraction and
coreference based on conditionally-trained undirected graphical models. We
discuss the advan... | computer science |
10,541 | The Author-Topic Model for Authors and Documents | cs.IR | We introduce the author-topic model, a generative model for documents that
extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include
authorship information. Each author is associated with a multinomial
distribution over topics and each topic is associated with a multinomial
distribution over words.... | computer science |
10,542 | A Hierarchical Graphical Model for Record Linkage | cs.LG | The task of matching co-referent records is known among other names as rocord
linkage. For large record-linkage problems, often there is little or no labeled
data available, but unlabeled data shows a reasonable clear structure. For such
problems, unsupervised or semi-supervised methods are preferable to supervised
met... | computer science |
10,543 | Stochastic optimization and sparse statistical recovery: An optimal
algorithm for high dimensions | stat.ML | We develop and analyze stochastic optimization algorithms for problems in
which the expected loss is strongly convex, and the optimum is (approximately)
sparse. Previous approaches are able to exploit only one of these two
structures, yielding an $\order(\pdim/T)$ convergence rate for strongly convex
objectives in $\pd... | computer science |
10,544 | Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | cs.LG | We propose a randomized block-coordinate variant of the classic Frank-Wolfe
algorithm for convex optimization with block-separable constraints. Despite its
lower iteration cost, we show that it achieves a similar convergence rate in
duality gap as the full Frank-Wolfe algorithm. We also show that, when applied
to the d... | computer science |
10,545 | Hierarchical Clustering using Randomly Selected Similarities | stat.ML | The problem of hierarchical clustering items from pairwise similarities is
found across various scientific disciplines, from biology to networking. Often,
applications of clustering techniques are limited by the cost of obtaining
similarities between pairs of items. While prior work has been developed to
reconstruct cl... | computer science |
10,546 | Causal Inference on Time Series using Structural Equation Models | stat.ML | Causal inference uses observations to infer the causal structure of the data
generating system. We study a class of functional models that we call Time
Series Models with Independent Noise (TiMINo). These models require independent
residual time series, whereas traditional methods like Granger causality
exploit the var... | computer science |
10,547 | Determinantal point processes for machine learning | stat.ML | Determinantal point processes (DPPs) are elegant probabilistic models of
repulsion that arise in quantum physics and random matrix theory. In contrast
to traditional structured models like Markov random fields, which become
intractable and hard to approximate in the presence of negative correlations,
DPPs offer efficie... | computer science |
10,548 | Identifying Users From Their Rating Patterns | cs.IR | This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track
2) for context-aware movie recommendation systems. The train dataset comprises
4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the
household groupings of a subset of the users. The test dataset comprises 5,450
rating... | computer science |
10,549 | Optimal Data Collection For Informative Rankings Expose Well-Connected
Graphs | stat.ML | Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method ... | computer science |
10,550 | Learning a peptide-protein binding affinity predictor with kernel ridge
regression | cs.LG | We propose a specialized string kernel for small bio-molecules, peptides and
pseudo-sequences of binding interfaces. The kernel incorporates
physico-chemical properties of amino acids and elegantly generalize eight
kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the
Radial Basis Function. We ... | computer science |
10,551 | A Method for Comparing Hedge Funds | cs.IR | The paper presents new machine learning methods: signal composition, which
classifies time-series regardless of length, type, and quantity; and
self-labeling, a supervised-learning enhancement. The paper describes further
the implementation of the methods on a financial search engine system to
identify behavioral simil... | computer science |
10,552 | Bio-Signals-based Situation Comparison Approach to Predict Pain | stat.AP | This paper describes a time-series-based classification approach to identify
similarities between bio-medical-based situations. The proposed approach allows
classifying collections of time-series representing bio-medical measurements,
i.e., situations, regardless of the type, the length and the quantity of the
time-ser... | computer science |
10,553 | Inverse Signal Classification for Financial Instruments | cs.LG | The paper presents new machine learning methods: signal composition, which
classifies time-series regardless of length, type, and quantity; and
self-labeling, a supervised-learning enhancement. The paper describes further
the implementation of the methods on a financial search engine system using a
collection of 7,881 ... | computer science |
10,554 | Matrix Completion via Max-Norm Constrained Optimization | cs.LG | Matrix completion has been well studied under the uniform sampling model and
the trace-norm regularized methods perform well both theoretically and
numerically in such a setting. However, the uniform sampling model is
unrealistic for a range of applications and the standard trace-norm relaxation
can behave very poorly ... | computer science |
10,555 | Sparse PCA through Low-rank Approximations | stat.ML | We introduce a novel algorithm that computes the $k$-sparse principal
component of a positive semidefinite matrix $A$. Our algorithm is combinatorial
and operates by examining a discrete set of special vectors lying in a
low-dimensional eigen-subspace of $A$. We obtain provable approximation
guarantees that depend on t... | computer science |
10,556 | Denoising Deep Neural Networks Based Voice Activity Detection | cs.LG | Recently, the deep-belief-networks (DBN) based voice activity detection (VAD)
has been proposed. It is powerful in fusing the advantages of multiple
features, and achieves the state-of-the-art performance. However, the deep
layers of the DBN-based VAD do not show an apparent superiority to the
shallower layers. In this... | computer science |
10,557 | Personalized News Recommendation with Context Trees | cs.IR | The profusion of online news articles makes it difficult to find interesting
articles, a problem that can be assuaged by using a recommender system to bring
the most relevant news stories to readers. However, news recommendation is
challenging because the most relevant articles are often new content seen by
few users. ... | computer science |
10,558 | Multivariate Temporal Dictionary Learning for EEG | cs.LG | This article addresses the issue of representing electroencephalographic
(EEG) signals in an efficient way. While classical approaches use a fixed Gabor
dictionary to analyze EEG signals, this article proposes a data-driven method
to obtain an adapted dictionary. To reach an efficient dictionary learning,
appropriate s... | computer science |
10,559 | Spectral Clustering with Epidemic Diffusion | cs.SI | Spectral clustering is widely used to partition graphs into distinct modules
or communities. Existing methods for spectral clustering use the eigenvalues
and eigenvectors of the graph Laplacian, an operator that is closely associated
with random walks on graphs. We propose a new spectral partitioning method that
exploi... | computer science |
10,560 | Gaussian Processes for Nonlinear Signal Processing | cs.LG | Gaussian processes (GPs) are versatile tools that have been successfully
employed to solve nonlinear estimation problems in machine learning, but that
are rarely used in signal processing. In this tutorial, we present GPs for
regression as a natural nonlinear extension to optimal Wiener filtering. After
establishing th... | computer science |
10,561 | Group-Sparse Model Selection: Hardness and Relaxations | cs.LG | Group-based sparsity models are proven instrumental in linear regression
problems for recovering signals from much fewer measurements than standard
compressive sensing. The main promise of these models is the recovery of
"interpretable" signals through the identification of their constituent groups.
In this paper, we e... | computer science |
10,562 | A General Iterative Shrinkage and Thresholding Algorithm for Non-convex
Regularized Optimization Problems | cs.LG | Non-convex sparsity-inducing penalties have recently received considerable
attentions in sparse learning. Recent theoretical investigations have
demonstrated their superiority over the convex counterparts in several sparse
learning settings. However, solving the non-convex optimization problems
associated with non-conv... | computer science |
10,563 | Recovering Non-negative and Combined Sparse Representations | math.NA | The non-negative solution to an underdetermined linear system can be uniquely
recovered sometimes, even without imposing any additional sparsity constraints.
In this paper, we derive conditions under which a unique non-negative solution
for such a system can exist, based on the theory of polytopes. Furthermore, we
deve... | computer science |
10,564 | Greedy Feature Selection for Subspace Clustering | cs.LG | Unions of subspaces provide a powerful generalization to linear subspace
models for collections of high-dimensional data. To learn a union of subspaces
from a collection of data, sets of signals in the collection that belong to the
same subspace must be identified in order to obtain accurate estimates of the
subspace s... | computer science |
10,565 | Node-Based Learning of Multiple Gaussian Graphical Models | stat.ML | We consider the problem of estimating high-dimensional Gaussian graphical
models corresponding to a single set of variables under several distinct
conditions. This problem is motivated by the task of recovering transcriptional
regulatory networks on the basis of gene expression data {containing
heterogeneous samples, s... | computer science |
10,566 | Sparse Factor Analysis for Learning and Content Analytics | stat.ML | We develop a new model and algorithms for machine learning-based learning
analytics, which estimate a learner's knowledge of the concepts underlying a
domain, and content analytics, which estimate the relationships among a
collection of questions and those concepts. Our model represents the
probability that a learner p... | computer science |
10,567 | Efficient Reinforcement Learning for High Dimensional Linear Quadratic
Systems | stat.ML | We study the problem of adaptive control of a high dimensional linear
quadratic (LQ) system. Previous work established the asymptotic convergence to
an optimal controller for various adaptive control schemes. More recently, for
the average cost LQ problem, a regret bound of ${O}(\sqrt{T})$ was shown, apart
form logarit... | computer science |
10,568 | Convex Tensor Decomposition via Structured Schatten Norm Regularization | stat.ML | We discuss structured Schatten norms for tensor decomposition that includes
two recently proposed norms ("overlapped" and "latent") for
convex-optimization-based tensor decomposition, and connect tensor
decomposition with wider literature on structured sparsity. Based on the
properties of the structured Schatten norms,... | computer science |
10,569 | Detecting Overlapping Temporal Community Structure in Time-Evolving
Networks | cs.SI | We present a principled approach for detecting overlapping temporal community
structure in dynamic networks. Our method is based on the following framework:
find the overlapping temporal community structure that maximizes a quality
function associated with each snapshot of the network subject to a temporal
smoothness c... | computer science |
10,570 | On the symmetrical Kullback-Leibler Jeffreys centroids | cs.IT | Due to the success of the bag-of-word modeling paradigm, clustering
histograms has become an important ingredient of modern information processing.
Clustering histograms can be performed using the celebrated $k$-means
centroid-based algorithm. From the viewpoint of applications, it is usually
required to deal with symm... | computer science |
10,571 | Universal Approximation Depth and Errors of Narrow Belief Networks with
Discrete Units | stat.ML | We generalize recent theoretical work on the minimal number of layers of
narrow deep belief networks that can approximate any probability distribution
on the states of their visible units arbitrarily well. We relax the setting of
binary units (Sutskever and Hinton, 2008; Le Roux and Bengio, 2008, 2010;
Mont\'ufar and A... | computer science |
10,572 | Independent Vector Analysis: Identification Conditions and Performance
Bounds | cs.LG | Recently, an extension of independent component analysis (ICA) from one to
multiple datasets, termed independent vector analysis (IVA), has been the
subject of significant research interest. IVA has also been shown to be a
generalization of Hotelling's canonical correlation analysis. In this paper, we
provide the ident... | computer science |
10,573 | Ensemble approaches for improving community detection methods | cs.LG | Statistical estimates can often be improved by fusion of data from several
different sources. One example is so-called ensemble methods which have been
successfully applied in areas such as machine learning for classification and
clustering. In this paper, we present an ensemble method to improve community
detection by... | computer science |
10,574 | Unmixing Incoherent Structures of Big Data by Randomized or Greedy
Decomposition | stat.ML | Learning big data by matrix decomposition always suffers from expensive
computation, mixing of complicated structures and noise. In this paper, we
study more adaptive models and efficient algorithms that decompose a data
matrix as the sum of semantic components with incoherent structures. We firstly
introduce "GO decom... | computer science |
10,575 | Scalable Probabilistic Entity-Topic Modeling | stat.ML | We present an LDA approach to entity disambiguation. Each topic is associated
with a Wikipedia article and topics generate either content words or entity
mentions. Training such models is challenging because of the topic and
vocabulary size, both in the millions. We tackle these problems using a novel
distributed infer... | computer science |
10,576 | Online Tensor Methods for Learning Latent Variable Models | cs.LG | We introduce an online tensor decomposition based approach for two latent
variable modeling problems namely, (1) community detection, in which we learn
the latent communities that the social actors in social networks belong to, and
(2) topic modeling, in which we infer hidden topics of text articles. We
consider decomp... | computer science |
10,577 | Semistochastic Quadratic Bound Methods | stat.ML | Partition functions arise in a variety of settings, including conditional
random fields, logistic regression, and latent gaussian models. In this paper,
we consider semistochastic quadratic bound (SQB) methods for maximum likelihood
inference based on partition function optimization. Batch methods based on the
quadrati... | computer science |
10,578 | Projection onto the probability simplex: An efficient algorithm with a
simple proof, and an application | cs.LG | We provide an elementary proof of a simple, efficient algorithm for computing
the Euclidean projection of a point onto the probability simplex. We also show
an application in Laplacian K-modes clustering. | computer science |
10,579 | A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries | stat.ML | We consider the problem of learning overcomplete dictionaries in the context
of sparse coding, where each sample selects a sparse subset of dictionary
elements. Our main result is a strategy to approximately recover the unknown
dictionary using an efficient algorithm. Our algorithm is a clustering-style
procedure, wher... | computer science |
10,580 | Exponentially Fast Parameter Estimation in Networks Using Distributed
Dual Averaging | cs.LG | In this paper we present an optimization-based view of distributed parameter
estimation and observational social learning in networks. Agents receive a
sequence of random, independent and identically distributed (i.i.d.) signals,
each of which individually may not be informative about the underlying true
state, but the... | computer science |
10,581 | Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized
Loss Minimization | stat.ML | We introduce a proximal version of the stochastic dual coordinate ascent
method and show how to accelerate the method using an inner-outer iteration
procedure. We analyze the runtime of the framework and obtain rates that
improve state-of-the-art results for various key machine learning optimization
problems including ... | computer science |
10,582 | Minimizing Finite Sums with the Stochastic Average Gradient | math.OC | We propose the stochastic average gradient (SAG) method for optimizing the
sum of a finite number of smooth convex functions. Like stochastic gradient
(SG) methods, the SAG method's iteration cost is independent of the number of
terms in the sum. However, by incorporating a memory of previous gradient
values the SAG me... | computer science |
10,583 | Efficient Orthogonal Tensor Decomposition, with an Application to Latent
Variable Model Learning | stat.ML | Decomposing tensors into orthogonal factors is a well-known task in
statistics, machine learning, and signal processing. We study orthogonal outer
product decompositions where the factors in the summands in the decomposition
are required to be orthogonal across summands, by relating this orthogonal
decomposition to the... | computer science |
10,584 | Mixed Membership Models for Time Series | stat.ME | In this article we discuss some of the consequences of the mixed membership
perspective on time series analysis. In its most abstract form, a mixed
membership model aims to associate an individual entity with some set of
attributes based on a collection of observed data. Although much of the
literature on mixed members... | computer science |
10,585 | Optimized projections for compressed sensing via rank-constrained
nearest correlation matrix | cs.IT | Optimizing the acquisition matrix is useful for compressed sensing of signals
that are sparse in overcomplete dictionaries, because the acquisition matrix
can be adapted to the particular correlations of the dictionary atoms. In this
paper a novel formulation of the optimization problem is proposed, in the form
of a ra... | computer science |
10,586 | Regularized Spectral Clustering under the Degree-Corrected Stochastic
Blockmodel | stat.ML | Spectral clustering is a fast and popular algorithm for finding clusters in
networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed
inspired variations on the algorithm that artificially inflate the node degrees
for improved statistical performance. The current paper extends the previous
statistical... | computer science |
10,587 | Network Anomaly Detection: A Survey and Comparative Analysis of
Stochastic and Deterministic Methods | stat.ML | We present five methods to the problem of network anomaly detection. These
methods cover most of the common techniques in the anomaly detection field,
including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and
clustering analysis. We evaluate all methods in a simulated network that
consists of nomi... | computer science |
10,588 | A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics | cs.LG | The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functi... | computer science |
10,589 | Distributed Online Learning in Social Recommender Systems | cs.SI | In this paper, we consider decentralized sequential decision making in
distributed online recommender systems, where items are recommended to users
based on their search query as well as their specific background including
history of bought items, gender and age, all of which comprise the context
information of the use... | computer science |
10,590 | Structured Convex Optimization under Submodular Constraints | cs.LG | A number of discrete and continuous optimization problems in machine learning
are related to convex minimization problems under submodular constraints. In
this paper, we deal with a submodular function with a directed graph structure,
and we show that a wide range of convex optimization problems under submodular
constr... | computer science |
10,591 | Stochastic Rank Aggregation | cs.LG | This paper addresses the problem of rank aggregation, which aims to find a
consensus ranking among multiple ranking inputs. Traditional rank aggregation
methods are deterministic, and can be categorized into explicit and implicit
methods depending on whether rank information is explicitly or implicitly
utilized. Surpri... | computer science |
10,592 | Modeling Documents with Deep Boltzmann Machines | cs.LG | We introduce a Deep Boltzmann Machine model suitable for modeling and
extracting latent semantic representations from a large unstructured collection
of documents. We overcome the apparent difficulty of training a DBM with
judicious parameter tying. This parameter tying enables an efficient
pretraining algorithm and a ... | computer science |
10,593 | Estimating Undirected Graphs Under Weak Assumptions | math.ST | We consider the problem of providing nonparametric confidence guarantees for
undirected graphs under weak assumptions. In particular, we do not assume
sparsity, incoherence or Normality. We allow the dimension $D$ to increase with
the sample size $n$. First, we prove lower bounds that show that if we want
accurate infe... | computer science |
10,594 | On statistics, computation and scalability | stat.ML | How should statistical procedures be designed so as to be scalable
computationally to the massive datasets that are increasingly the norm? When
coupled with the requirement that an answer to an inferential question be
delivered within a certain time budget, this question has significant
repercussions for the field of s... | computer science |
10,595 | Structured Block Basis Factorization for Scalable Kernel Matrix
Evaluation | stat.ML | Kernel matrices are popular in machine learning and scientific computing, but
they are limited by their quadratic complexity in both construction and
storage. It is well-known that as one varies the kernel parameter, e.g., the
width parameter in radial basis function kernels, the kernel matrix changes
from a smooth low... | computer science |
10,596 | Risk Bounds For Mode Clustering | math.ST | Density mode clustering is a nonparametric clustering method. The clusters
are the basins of attraction of the modes of a density estimator. We study the
risk of mode-based clustering. We show that the clustering risk over the
cluster cores --- the regions where the density is high --- is very small even
in high dimens... | computer science |
10,597 | Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology
Dependence | cs.LG | Data in the form of pairwise comparisons arises in many domains, including
preference elicitation, sporting competitions, and peer grading among others.
We consider parametric ordinal models for such pairwise comparison data
involving a latent vector $w^* \in \mathbb{R}^d$ that represents the
"qualities" of the $d$ ite... | computer science |
10,598 | Newton Sketch: A Linear-time Optimization Algorithm with
Linear-Quadratic Convergence | math.OC | We propose a randomized second-order method for optimization known as the
Newton Sketch: it is based on performing an approximate Newton step using a
randomly projected or sub-sampled Hessian. For self-concordant functions, we
prove that the algorithm has super-linear convergence with exponentially high
probability, wi... | computer science |
10,599 | Simultaneous Clustering and Model Selection for Multinomial
Distribution: A Comparative Study | cs.LG | In this paper, we study different discrete data clustering methods, which use
the Model-Based Clustering (MBC) framework with the Multinomial distribution.
Our study comprises several relevant issues, such as initialization, model
estimation and model selection. Additionally, we propose a novel MBC method by
efficientl... | computer science |
10,600 | Bayesian Sparse Tucker Models for Dimension Reduction and Tensor
Completion | cs.LG | Tucker decomposition is the cornerstone of modern machine learning on
tensorial data analysis, which have attracted considerable attention for
multiway feature extraction, compressive sensing, and tensor completion. The
most challenging problem is related to determination of model complexity (i.e.,
multilinear rank), e... | computer science |
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