Group Lasso with Overlap and Graph Lasso
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 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 theoretical properties of the estimator, and illustrate its behavior on simulated and breast cancer gene expression data.
Cite
Text
Jacob et al. "Group Lasso with Overlap and Graph Lasso." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553431Markdown
[Jacob et al. "Group Lasso with Overlap and Graph Lasso." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/jacob2009icml-group/) doi:10.1145/1553374.1553431BibTeX
@inproceedings{jacob2009icml-group,
title = {{Group Lasso with Overlap and Graph Lasso}},
author = {Jacob, Laurent and Obozinski, Guillaume and Vert, Jean-Philippe},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {433-440},
doi = {10.1145/1553374.1553431},
url = {https://mlanthology.org/icml/2009/jacob2009icml-group/}
}