A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel hilbert spaces

Masa Aki Takizawa, Masahiro Yukawa, Cedric Richard

    研究成果: Conference contribution

    5 被引用数 (Scopus)

    抄録

    This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It also includes stability conditions in the mean and mean-square sense. The present study is based on the analysis of the kernel normalized least mean square (KNLMS) algorithm initially proposed by Chen et al. Simulation results validate the analysis.

    本文言語English
    ホスト出版物のタイトルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ2001-2005
    ページ数5
    2015-August
    ISBN(印刷版)9781467369978
    DOI
    出版ステータスPublished - 2015 8月 4
    イベント40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
    継続期間: 2014 4月 192014 4月 24

    Other

    Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
    国/地域Australia
    CityBrisbane
    Period14/4/1914/4/24

    ASJC Scopus subject areas

    • 信号処理
    • ソフトウェア
    • 電子工学および電気工学

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