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 Efficient methods for overlapping group lasso. MedLine Citation: PMID:  23868773     Owner:  NLM     Status:  In-Data-Review Abstract/OtherAbstract: The group Lasso is an extension of the Lasso for feature selection on (predefined) nonoverlapping groups of features. The nonoverlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general formulation where groups of features are given, potentially with overlaps between the groups. The resulting optimization is, however, much more challenging to solve due to the group overlaps. In this paper, we consider the efficient optimization of the overlapping group Lasso penalized problem. We reveal several key properties of the proximal operator associated with the overlapping group Lasso, and compute the proximal operator by solving the smooth and convex dual problem, which allows the use of the gradient descent type of algorithms for the optimization. Our methods and theoretical results are then generalized to tackle the general overlapping group Lasso formulation based on the $(\ell_q)$ norm. We further extend our algorithm to solve a nonconvex overlapping group Lasso formulation based on the capped norm regularization, which reduces the estimation bias introduced by the convex penalty. We have performed empirical evaluations using both a synthetic and the breast cancer gene expression dataset, which consists of 8,141 genes organized into (overlapping) gene sets. Experimental results show that the proposed algorithm is more efficient than existing state-of-the-art algorithms. Results also demonstrate the effectiveness of the nonconvex formulation for overlapping group Lasso. Authors: Lei Yuan; Jun Liu; Jieping Ye Related Documents : 16496023 - Capturing complex 3d tissue physiology in vitro.8456523 - Correlations between acoustic and texture parameters from rf and b-mode liver echograms.18311853 - A hypothesis-free multiple scan statistic with variable window. Publication Detail: Type:  Journal Article Journal Detail: Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  35     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2013 Sep Date Detail: Created Date:  2013-07-22     Completed Date:  -     Revised Date:  - Medline Journal Info: Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States Other Details: Languages:  eng     Pagination:  2104-16     Citation Subset:  IM Affiliation: Arizona State University, Tempe. Export Citation: APA/MLA Format     Download EndNote     Download BibTex MeSH Terms Descriptor/Qualifier:

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