Iterative optimization of convex divergence: Applications to independent component analysis

Yasuo Matsuyama

    研究成果: Conference contribution

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルIEEE International Symposium on Information Theory - Proceedings
    ページ214
    ページ数1
    出版ステータスPublished - 2003
    イベントProceedings 2003 IEEE International Symposium on Information Theory (ISIT) - Yokohama, Japan
    継続期間: 2003 6 292003 7 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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