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73
Online learning for matrix factorization and sparse coding
, 2010
"... Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the largescale matrix factorization problem that consists of learning the basis set in order to ad ..."
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Cited by 319 (32 self)
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Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the largescale matrix factorization problem that consists of learning the basis set in order to adapt it to specific data. Variations of this problem include dictionary learning in signal processing, nonnegative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. A proof of convergence is presented, along with experiments with natural images and genomic data demonstrating that it leads to stateoftheart performance in terms of speed and optimization for both small and large data sets.
Structured variable selection with sparsityinducing norms
, 2011
"... We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsityinducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual ℓ1norm and the group ℓ1norm by allowing the subsets to ov ..."
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Cited by 197 (32 self)
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We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsityinducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual ℓ1norm and the group ℓ1norm by allowing the subsets to overlap. This leads to a specific set of allowed nonzero patterns for the solutions of such problems. We first explore the relationship between the groups defining the norm and the resulting nonzero patterns, providing both forward and backward algorithms to go back and forth from groups to patterns. This allows the design of norms adapted to specific prior knowledge expressed in terms of nonzero patterns. We also present an efficient active set algorithm, and analyze the consistency of variable selection for leastsquares linear regression in low and highdimensional settings.
F.: Proximal methods for sparse hierarchical dictionary learning
 In: ICML
"... This paper proposes to combine two approaches for modeling data admitting sparse representations: On the one hand, dictionary learning has proven very effective for various signal restoration and representation tasks. On the other hand, recent work on structured sparsity provides a natural framework ..."
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Cited by 126 (23 self)
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This paper proposes to combine two approaches for modeling data admitting sparse representations: On the one hand, dictionary learning has proven very effective for various signal restoration and representation tasks. On the other hand, recent work on structured sparsity provides a natural framework for modeling dependencies between dictionary elements. We propose to combine these approaches to learn dictionaries embedded in a hierarchy. We show that the proximal operator for the treestructured sparse regularization that we consider can be computed exactly in linear time with a primaldual approach, allowing the use of accelerated gradient methods. Experiments show that for natural image patches, learned dictionary elements organize themselves naturally in such a hierarchical structure, leading to an improved performance for restoration tasks. When applied to text documents, our method learns hierarchies of topics, thus providing a competitive alternative to probabilistic topic models. Learned sparse representations, initially introduced by Olshausen and Field [1997], have been the focus of much research in machine learning, signal processing and neuroscience, leading to stateoftheart algorithms for several problems in image processing. Modeling signals as a linear combination of a
Structured sparsityinducing norms through submodular functions
 IN ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2010
"... Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turnedinto a convex optimization problem byreplacing the cardinality function by its convex en ..."
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Cited by 62 (13 self)
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Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial selection problem is often turnedinto a convex optimization problem byreplacing the cardinality function by its convex envelope (tightest convex lower bound), in this case the ℓ1norm. In this paper, we investigate more general setfunctions than the cardinality, that may incorporate prior knowledge or structural constraints which are common in many applications: namely, we show that for nonincreasing submodular setfunctions, the corresponding convex envelope can be obtained from its Lovász extension, a common tool in submodular analysis. This defines a family of polyhedral norms, for which we provide generic algorithmic tools (subgradients and proximal operators) and theoretical results (conditions for support recovery or highdimensional inference). By selecting specific submodular functions, we can give a new interpretation to known norms, such as those based on rankstatistics or grouped norms with potentially overlapping groups; we also define new norms, in particular ones that can be used as nonfactorial priors for supervised learning.
Structured Sparsity through Convex Optimization
"... Abstract. Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection admits a convex relaxation through the regularization by the ℓ1norm. In this paper, we cons ..."
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Cited by 48 (7 self)
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Abstract. Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection admits a convex relaxation through the regularization by the ℓ1norm. In this paper, we consider situations where we are not only interested in sparsity, but where some structural prior knowledge is available as well. We show that the ℓ1norm can then be extended to structured norms built on either disjoint or overlapping groups of variables, leading to a flexible framework that can deal with various structures. We present applications to unsupervised learning, for structured sparse principal component analysis and hierarchical dictionary learning, and to supervised learning in the context of nonlinear variable selection. Key words and phrases: Sparsity, convex optimization. 1.
Learning with submodular functions: A convex optimization perspective
, 2011
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Factorized Latent Spaces with Structured Sparsity
"... Recent approaches to multiview learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities. Unfortunately, these appr ..."
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Cited by 41 (3 self)
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Recent approaches to multiview learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities. Unfortunately, these approaches involve minimizing nonconvex objective functions. In this paper, we propose an approach to learning such factorized representations inspired by sparse coding techniques. In particular, we show that structured sparsity allows us to address the multiview learning problem by alternately solving two convex optimization problems. Furthermore, the resulting factorized latent spaces generalize over existing approaches in that they allow having latent dimensions shared between any subset of the views instead of between all the views only. We show that our approach outperforms stateoftheart methods on the task of human pose estimation. 1
Optimal detection of sparse principal components in high dimension
, 2013
"... We perform a finite sample analysis of the detection levels for sparse principal components of a highdimensional covariance matrix. Our minimax optimal test is based on a sparse eigenvalue statistic. Alas, computing this test is known to be NPcomplete in general, and we describe a computationally ..."
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Cited by 39 (5 self)
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We perform a finite sample analysis of the detection levels for sparse principal components of a highdimensional covariance matrix. Our minimax optimal test is based on a sparse eigenvalue statistic. Alas, computing this test is known to be NPcomplete in general, and we describe a computationally efficient alternative test using convex relaxations. Our relaxation is also proved to detect sparse principal components at near optimal detection levels, and it performs well on simulated datasets. Moreover, using polynomial time reductions from theoretical computer science, we bring significant evidence that our results cannot be improved, thus revealing an inherent trade off between statistical and computational performance.