An efficient kernel normalized least mean square algorithm with compactly supported kernel

Osamu Toda, Masahiro Yukawa

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

1 Citation (Scopus)

Abstract

We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain ℓ1-ball. In the second step, it identifies the exact support by detailed examinations based on an ℓ2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of ℓ2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3367-3371
Number of pages5
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 2015 Aug 4
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 2014 Apr 192014 Apr 24

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period14/4/1914/4/24

Keywords

  • Compactly supported function
  • Gaussian kernel
  • Kernel learning
  • Positive definite function
  • Radial basis function

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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