A better metric in kernel adaptive filtering

Airi Takeuchi, Masahiro Yukawa, Klaus Robert Müller

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    The metric in the reproducing kernel Hilbert space (RKHS) is known to be given by the Gram matrix (which is also called the kernel matrix). It has been reported that the metric leads to a decorrelation of the kernelized input vector because its autocorrelation matrix can be approximated by the (down scaled) squared Gram matrix subject to some condition. In this paper, we derive a better metric (a best one under the condition) based on the approximation, and present an adaptive algorithm using the metric. Although the algorithm has quadratic complexity, we present its linear-complexity version based on a selective updating strategy. Numerical examples validate the approximation in a practical scenario, and show that the proposed metric yields fast convergence and tracking performance.

    本文言語English
    ホスト出版物のタイトル2016 24th European Signal Processing Conference, EUSIPCO 2016
    出版社European Signal Processing Conference, EUSIPCO
    ページ1578-1582
    ページ数5
    2016-November
    ISBN(電子版)9780992862657
    DOI
    出版ステータスPublished - 2016 11 28
    イベント24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
    継続期間: 2016 8 282016 9 2

    Other

    Other24th European Signal Processing Conference, EUSIPCO 2016
    国/地域Hungary
    CityBudapest
    Period16/8/2816/9/2

    ASJC Scopus subject areas

    • 信号処理
    • 電子工学および電気工学

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