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inria-00628498, version 1

Group Lasso with Overlaps: the Latent Group Lasso approach

Guillaume Obozinski (Author to contact preferably) 12, Laurent Jacob (Author to contact preferably) 3, Jean-Philippe Vert () 45

(2011)

Abstract: We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variables. We call the obtained formulation latent group Lasso, since it is based on applying the usual group Lasso penalty on a set of latent variables. A detailed analysis of the norm and its properties is presented and we characterize conditions under which the set of groups associated with latent variables are correctly identi ed. We motivate and discuss the delicate choice of weights associated to each group, and illustrate this approach on simulated data and on the problem of breast cancer prognosis from gene expression data.

  • Domain : Statistics/Other Statistics
    Computer Science/Machine Learning
  • Keywords : group Lasso – sparsity – structured sparsity – graph – support recovery – block norm – regularization – feature selection
 
  • inria-00628498, version 1
  • oai:hal.inria.fr:inria-00628498
  • From: 
  • Submitted on: Monday, 3 October 2011 14:49:33
  • Updated on: Monday, 3 October 2011 18:49:47
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