The benefit of group sparsity

Junzhou Huang, Tong Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper develops a theory for group Lasso using a concept called strong group sparsity. Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals. This provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is consistent with the data. Moreover, the theory predicts some limitations of the group Lasso formulation that are confirmed by simulation studies.

Original languageEnglish (US)
Pages (from-to)1978-2004
Number of pages27
JournalAnnals of Statistics
Volume38
Issue number4
DOIs
StatePublished - Aug 2010
Externally publishedYes

Keywords

  • Group Lasso
  • Group sparsity
  • L regularization
  • Lasso
  • Parameter estimation
  • Regression
  • Sparsity
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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