Group Lasso with Overlap and Graph Lasso

Group Lasso with Overlap and Graph Lasso

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by Laurent Jacob , Guillaume Obozinski

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@MISC{Jacob_grouplasso,
    author = {Laurent Jacob and Guillaume Obozinski},
    title = {Group Lasso with Overlap and Graph Lasso},
    year = {}
}

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Abstract

We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is typically a union of potentially overlapping groups of covariates defined a priori, or a set of covariates which tend to be connected to each other when a graph of covariates is given. We study theoretical properties of the estimator, and illustrate its behavior on simulated and breast cancer gene expression data. 1.

Citations

1089	Atomic decomposition by basis pursuit - Chen, Donoho, et al. - 1998
998	Regression shrinkage and selection via the lasso - Tibshirani - 1996
239	Model selection and estimation in regression with grouped variables - Yuan, Lin
132	Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles - Subramanian, Tamayo, et al.
95	Asymptotics for lasso-type estimators - Knight, Fu - 2000
82	Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ1-constrained quadratic programs - Wainwright
81	Consistency of the group lasso and multiple kernel learning - Bach
75	The group lasso for logistic regression - Meier, Geer, et al.
61	Grouped and hierarchical model selection through composite absolute penalties - Zhao, Rocha, et al.
48	A gene expression signature as a predictor of survival in breast - Vijver - 2002
46	Joint covariate selection and joint subspace selection for multiple classification problems - Obozinski, Taskar, et al.
45	Exploring large feature space with hierarchical multiple kernel learning - Bach
45	Structured variable selection with sparsity-inducing norms - Jenatton, Audibert, et al. - 2009
31	Network-based classification of breast cancer metastasis - Chuang, Lee, et al.
18	The group-lasso for generalized linear models: uniqueness of solutions and efficient algorithms - Roth, Fischer - 2008
8	Classification of microarray data using gene networks - Rapaport - 2007
7	The generalized LASSO: a wrapper approach to gene selection for microarray data - Roth - 2002
2	An implicit function theorem - Kumagai - 1980