Group Lasso with Overlaps and Graph Lasso
laurent jacob, Guillaume Obozinski and Jean-Philippe Vert
In: Proceedings of the 26th Annual International Conference on Machine Learning ACM International Conference Proceeding Series , 382 . (2009) ACM , pp. 433-440.

Abstract

We propose a new penalty function which, when used as regularization for empirical risk mini- mization procedures, leads to sparse estimators. The support of the sparse vector is typically a union of potentially overlapping groups of co- variates 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 theo- retical properties of the estimator, and illustrate its behavior on simulated and breast cancer gene expression data.

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