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TREE-GUIDED GROUP LASSO FOR MULTI-RESPONSE. REGRESSION WITH STRUCTURED SPARSITY, WITH AN. APPLICATION TO EQTL MAPPING1. BY SEYOUNG KIM AND ERIC P. XING. 2. Carnegie Mellon University. We consider the problem of estimating a sparse multi-response re- gression function, with an
Search Machine Learning Repository: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity Authors: Seyoung Kim and Eric P. Xing Conference: Proceedings of the 27th International Conference on Machine Learning (ICML-10) Year: 2010. Pages: 543-550 [pdf] [BibTeX] · authors venues years
Our structured regularization is based on a grouplasso penalty, where groups are defined with respect to the tree structure. We describe a systematic weighting scheme for the groups in the penalty such that each output variable is penalized in a balanced manner even if the groups overlap.
We consider the problem of learning a sparse multi-task regression, where the structure in the outputs can be represented as a tree with leaf nodes as outputs and internal nodes as clusters of the outputs at multiple granularity. Our goal is to Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity.
On Jan 1, 2010 S. Kim (and others) published: Tree-guided group lasso for multi-task regression with structured sparsity.
Tree-Guided Group Lasso for Multi-Task Regression with. Structured Sparsity. Seyoung Kim sssykim@cs.cmu.edu. Eric P. Xing epxing@cs.cmu.edu. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Abstract. We consider the problem of learning a sparse multi-task regression, where the
We propose a tree-guided group lasso, or tree lasso, for estimating such structured sparsity under multi-response regression by employing a novel penalty function constructed from the tree. We describe a systematic weighting scheme for the overlapping groups in the tree-penalty such that each regression coefficient is
8 Sep 2009 We propose a tree-guided group lasso, or tree lasso, for estimating such structured sparsity under multi-response regression by employing a novel penalty function constructed from the tree. We describe a systematic weighting scheme for the overlapping groups in the tree-penalty such that each regression
21 Jun 2010 Tree-guided group lasso for multi-task regression with structured sparsity, 2010 Article. Bibliometrics Data Bibliometrics. · Citation Count: 0 · Downloads (cumulative): 0 · Downloads (12 Months): 0 · Downloads (6 Weeks): 0
We propose tree-guided group lasso, or tree lasso, for estimating such structured sparsity under multi-response regression by employing a novel penalty function constructed from the tree. We describe a systematic weighting scheme for the overlapping groups in the tree-penalty such that each regression coefficient is

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