Hierarchical Sparsity Within And Across Overlapping Groups

2018 IEEE Statistical Signal Processing Workshop (SSP)(2018)

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摘要
Recently, different penalties have been proposed for signals whose non-zero coefficients reside in a small number of groups, where within each group, only few of the coefficients are active. In this paper, we extend such a penalty, and introduce an additional layer of grouping on the coefficients. Specifically, we first partition the signal into groups, and then apply the penalty on the ℓ 2 norms of the groups. We discuss how this extended penalty can be used in energy minimization formulations, and demonstrate the effects of the proposed extension on a dereverberation experiment.
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关键词
Sparsity within and across groups,elitist Lasso,group sparsity,mixed norm,overlapping groups
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