An efficient parallel variable-metric projection algorithm based on set-theoretic and Krylov-proportionate adaptive filtering techniques

Masahiro Yukawa, Isao Yamada

Research output: Contribution to conferencePaper

Abstract

We blend two adaptive filtering techniques for further efficiency: the set-theoretic adaptive filter (STAF, Yamada et al. 2002) and the Krylov-proportionate adaptive filter (KPAF, Yukawa 2009). Although the ideas behind these techniques are quite different from each other, there is a way to blend them together by noticing that KPAF can be seen as a sort of 'variable-metric' projection algorithm. We propose a blended algorithm named set-theoretic Krylov-proportionate adaptive filter (SKAF), which features iterative parallel variable-metric projection onto well-designed closed convex sets. We present comparisons in complexity and mean square error (MSE) performance, showing significant advantages of the proposed algorithm over the existing algorithms.

Original languageEnglish
Pages418-421
Number of pages4
Publication statusPublished - 2009 Dec 1
Externally publishedYes
EventAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009 - Sapporo, Japan
Duration: 2009 Oct 42009 Oct 7

Other

OtherAsia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009
CountryJapan
CitySapporo
Period09/10/409/10/7

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering
  • Communication

Fingerprint Dive into the research topics of 'An efficient parallel variable-metric projection algorithm based on set-theoretic and Krylov-proportionate adaptive filtering techniques'. Together they form a unique fingerprint.

  • Cite this

    Yukawa, M., & Yamada, I. (2009). An efficient parallel variable-metric projection algorithm based on set-theoretic and Krylov-proportionate adaptive filtering techniques. 418-421. Paper presented at Asia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009, Sapporo, Japan.