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 publicationEuropean Signal Processing Conference
Pages2183-2187
Number of pages5
Publication statusPublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest
Duration: 2012 Aug 272012 Aug 31

Other

Other20th European Signal Processing Conference, EUSIPCO 2012
CityBucharest
Period12/8/2712/8/31

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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

Cite this

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