A variational approach for sparse component estimation and low-rank matrix recovery

Zhaofu Chen, Rafael Molina, Aggelos K. Katsaggelos

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We propose a variational Bayesian based algorithm for the estimation of the sparse component of an outliercorrupted low-rank matrix, when linearly transformed composite data are observed. The model constitutes a generalization of robust principal component analysis. The problem considered herein is applicable in various practical scenarios, such as foreground detection in blurred and noisy video sequences and detection of network anomalies among others. The proposed algorithm models the low-rank matrix and the sparse component using a hierarchical Bayesian framework, and employs a variational approach for inference of the unknowns. The effectiveness of the proposed algorithm is demonstrated using real life experiments, and its performance improvement over regularization based approaches is shown.

Original languageEnglish (US)
Pages (from-to)600-611
Number of pages12
JournalJournal of Communications
Volume8
Issue number9
DOIs
StatePublished - Sep 1 2013

Keywords

  • Bayesian inference
  • Foreground detection
  • Network anomaly detection
  • Robust principal component analysis
  • Variational approach

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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