Group lasso with overlap and graph lasso

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.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Laurent Jacob, Guillaume Obozinski, Jean-Philippe Vert
• Keywords: Empirical Risk Minimization, ICML 2009, Machine Learning, Sparse Vector