Group Lasso with Overlaps and Graph Lasso

author: Laurent Jacob, Department of Statistics, UC Berkeley
published: Aug. 26, 2009,   recorded: July 2009,   views: 8724


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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.

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