An EM algorithm for learning sparse and overcomplete representations

Mingjun Zhong, Huanwen Tang, Hongjun Chen, Yiyuan Tang

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients.

Original languageEnglish
Pages (from-to)469-476
Number of pages8
JournalNeurocomputing
Volume57
Issue number1-4
DOIs
StatePublished - Mar 2004

Keywords

  • EM algorithm
  • Maximum a posteriori
  • Overcomplete representations

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