A better metric in kernel adaptive filtering

Airi Takeuchi, Masahiro Yukawa, Klaus Robert Müller

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

    2 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
    PublisherEuropean Signal Processing Conference, EUSIPCO
    Number of pages5
    ISBN (Electronic)9780992862657
    Publication statusPublished - 2016 Nov 28
    Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
    Duration: 2016 Aug 282016 Sept 2


    Other24th European Signal Processing Conference, EUSIPCO 2016

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering


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