Iterative optimization of convex divergence: Applications to independent component analysis

Yasuo Matsuyama

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

    Abstract

    Iterative optimization of convex divergence is discussed. The convex divergence is used as a measure of independence for ICA algorithms. An additional method to incorporate supervisory information to reduce the ICA's permutation indeterminacy is also given. Speed of the algorithm is examined using a set of simulated data and brain fMRI data.

    Original languageEnglish
    Title of host publicationIEEE International Symposium on Information Theory - Proceedings
    Pages214
    Number of pages1
    Publication statusPublished - 2003
    EventProceedings 2003 IEEE International Symposium on Information Theory (ISIT) - Yokohama, Japan
    Duration: 2003 Jun 292003 Jul 4

    Other

    OtherProceedings 2003 IEEE International Symposium on Information Theory (ISIT)
    CountryJapan
    CityYokohama
    Period03/6/2903/7/4

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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  • Cite this

    Matsuyama, Y. (2003). Iterative optimization of convex divergence: Applications to independent component analysis. In IEEE International Symposium on Information Theory - Proceedings (pp. 214)