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A fixed-point algorithm for nonnegative independent component analysis

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a fixed-point algorithm for nonnegative independent component analysis, based on the mutual independency of source signals and 'nonpositive 'parts of source signals. The algorithm is computationally simple, provides fast convergence and does not need choose any learning step sizes. Simulations by independent source signals which are nonnegative and well grounded verify the efficient implementation of the proposed method.

Original languageEnglish
Title of host publication5th International Conference on Natural Computation, ICNC 2009
PublisherIEEE Computer Society
Pages482-485
Number of pages4
ISBN (Print)9780769537368
DOIs
StatePublished - 2009
Event5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China
Duration: 14 Aug 200916 Aug 2009

Publication series

Name5th International Conference on Natural Computation, ICNC 2009
Volume2

Conference

Conference5th International Conference on Natural Computation, ICNC 2009
Country/TerritoryChina
CityTianjian
Period14/08/0916/08/09

Keywords

  • Blind source separation (BSS)
  • Fixed-point algorithm
  • Independent component analysis (ICA)
  • Nonnegative independent component analysis

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