A parameter-free Cauchy-Schwartz information measure for independent component analysis

Lei Sun, Badong Chen, Kar Ann Ton, Zhiping Lin

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

Abstract

Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2524-2528
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 18 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Blind Signal Separation
  • Independent Component Analysis
  • Information Measure
  • Probability Survival Distribution

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