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

Masahiro Yukawa; Isao Yamada

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

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	418-421
Number of pages	4
Publication status	Published - 2009 Dec 1
Externally published	Yes
Event	Asia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009 - Sapporo, Japan Duration: 2009 Oct 4 → 2009 Oct 7

Other

Other	Asia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009
Country/Territory	Japan
City	Sapporo
Period	09/10/4 → 09/10/7

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Electrical and Electronic Engineering
Communication

Cite this

An efficient parallel variable-metric projection algorithm based on set-theoretic and Krylov-proportionate adaptive filtering techniques. / Yukawa, Masahiro; Yamada, Isao.

2009. 418-421 Paper presented at Asia-Pacific Signal and Information Processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009, Sapporo, Japan.

Research output: Contribution to conference › Paper › peer-review

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AB - 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.

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

Abstract

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