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A semi-blind EM algorithm for overcomplete ICA

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Overcomplete independent component analysis (ICA) is a challenge of ICA to estimate more sources from less mixtures. The statistical properties of the sonrces snch as sparsity are often assnmed to solve the problem. Other available information about the sources such as waveform, however, is scarcely used. Motivated by the fact that semi- blind ICA in complete case can improve the potential of ICA by incorporating source information, this paper proposes a semi-blind algorithm for overcomplete ICA by explicitly utilizing waveform information about some sources. An approximate expectation-maximization (EM) algorithm is explored to provide normal cost function of the semi-blind algorithm while the prior information is utilized to form an extended one. Computer simulations results demonstrate that the proposed algorithm has much improved performance in SNR, convergence speed, and elimination of order ambiguity compared to the original EM algorithm.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
PublisherIEEE
Pages1733-1736
Number of pages4
ISBN (Print)9781424423545
DOIs
Publication statusPublished - 26 May 2009
Externally publishedYes
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan
Duration: 19 Apr 200924 Apr 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan
CityTaipei
Period19/04/0924/04/09

Keywords

  • EM algorithm
  • Independent component analysis
  • Overcomplete ICA
  • Semi-blind ICA

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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