Bayesian group-sparse modeling and variational inference

S. Derin Babacan, Shinichi Nakajima, Minh N. Do

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a general class of multivariate priors for group-sparse modeling within the Bayesian framework. We show that special cases of this class correspond to multivariate versions of several classical priors used for sparse modeling. Hence, this general prior formulation is helpful in analyzing the properties of different modeling approaches and their connections. We derive the estimation procedures with these priors using variational inference for fully Bayesian estimation. In addition, we discuss the differences between the proposed inference and deterministic inference approaches with these priors. Finally, we show the flexibility of this modeling by considering several extensions such as multiple measurements, within-group correlations, and overlapping groups.

Original languageEnglish (US)
Article number6804013
Pages (from-to)2906-2921
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume62
Issue number11
DOIs
StatePublished - Jul 1 2014

Keywords

  • Bayes methods
  • Group-sparsity
  • Variational inference

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

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