A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel hilbert spaces

Masa Aki Takizawa, Masahiro Yukawa, Cedric Richard

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

5 Citations (Scopus)

Abstract

This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It also includes stability conditions in the mean and mean-square sense. The present study is based on the analysis of the kernel normalized least mean square (KNLMS) algorithm initially proposed by Chen et al. Simulation results validate the analysis.

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.
Pages2001-2005
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

  • kernel adaptive filter
  • performance analysis
  • reproducing kernel Hilbert space
  • the KLMS algorithm

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel hilbert spaces'. Together they form a unique fingerprint.

Cite this