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Group Lasso with Overlaps and Graph Lasso
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
