Structured sparsity regularization

Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity… (More)
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Topic mentions per year

1993-2018
05101519932018

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2016
2016
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co… (More)
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2013
2013
Multi-linear techniques using tensor decompositions provide a unifying framework for the high-dimensional data analysis. Sparsity… (More)
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Review
2012
Review
2012
This paper reviews our recent work on the application of a class of techniques known as ADMM (alternating direction method of… (More)
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2011
2011
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data… (More)
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Highly Cited
2010
Highly Cited
2010
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are… (More)
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2010
2010
Proximal methods have recently been shown to provide effective optimization procedures to solve the variational problems defining… (More)
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Highly Cited
2010
Highly Cited
2010
We consider the problem of learning a sparse multi-task regression, where the structure in the outputs can be represented as a… (More)
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Highly Cited
2010
Highly Cited
2010
We consider the tree structured group Lasso where the structure over the features can be represented as a tree with leaf nodes as… (More)
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2009
2009
In this paper we propose a general framework to characterize and solve the optimization problems underlying a large class of… (More)
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2009
2009
We consider the problem of learning a sparse multi-task regression with an application to a genetic association mapping problem… (More)
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