An efficient kernel adaptive filtering algorithm using hyperplane projection along affine subspace

Masahiro Yukawa, Ryu Ichiro Ishii

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

13 Citations (Scopus)

Abstract

We propose a novel kernel adaptive filtering algorithm that selectively updates a few coefficients at each iteration by projecting the current filter onto the zero instantaneous-error hyperplane along a certain time-dependent affine subspace. Coherence is exploited for selecting the coefficients to be updated as well as for measuring the novelty of new data. The proposed algorithm is a natural extension of the normalized kernel least mean squares algorithm operating iterative hyperplane projections in a reproducing kernel Hilbert space. The proposed algorithm enjoys low computational complexity. Numerical examples indicate high potential of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages2183-2187
Number of pages5
Publication statusPublished - 2012 Nov 27
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 2012 Aug 272012 Aug 31

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
CountryRomania
CityBucharest
Period12/8/2712/8/31

Keywords

  • kernel adaptive filter
  • normalized kernel least mean square algorithm
  • projection algorithms
  • reproducing kernel Hilbert space

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Yukawa, M., & Ishii, R. I. (2012). An efficient kernel adaptive filtering algorithm using hyperplane projection along affine subspace. In Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012 (pp. 2183-2187). [6333933] (European Signal Processing Conference).