Extraction of independent components from hybrid mixture: KuicNet learning algorithm and applications

S. Y. Kung, C. Mejuto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

A hybrid mixture is a mixture of supergaussian, gaussian, and subgaussian independent components (ICs). This paper addresses extraction of ICs from a hybrid mixture. There are two kinds of (single-output vs. all-outputs) kurtosis function to be considered as a contrast function. We advocate the former approach due to its (1) simple and closed-form analysis, and (2) numerical convergence and computational saving. Via this approach, all (and only) the positive local maxima (resp. negative local minima) can yield supergaussian (resp, subgaussian) ICs from any mixture (Kung 1997). We also propose a network algorithm, kurtosis-based independent component network (KuicNet), for recursively extracting ICs. Numerical and convergence properties are analyzed and several application examples demonstrated.

Original languageEnglish (US)
Title of host publicationProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Pages1209-1212
Number of pages4
DOIs
StatePublished - Dec 1 1998
Event1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 - Seattle, WA, United States
Duration: May 12 1998May 15 1998

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
CountryUnited States
CitySeattle, WA
Period5/12/985/15/98

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Kung, S. Y., & Mejuto, C. (1998). Extraction of independent components from hybrid mixture: KuicNet learning algorithm and applications. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 (pp. 1209-1212). [675488] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). https://doi.org/10.1109/ICASSP.1998.675488