Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method

Masahiro Yukawa; Rodrigo C. De Lamare; Isao Yamada

doi:10.1109/ACSSC.2007.4487244

Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method

Masahiro Yukawa, Rodrigo C. De Lamare, Isao Yamada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a novel reduced-rank adaptive filtering algorithm based on set-theoretic adaptive filtering. We discuss the orthonormality of the transformation (rank-reduction) matrix. We present, under the assumption that the transformation matrix has an orthonormal structure, an interpretation of the proposed algorithm in the original (fullsize) vector space. The interpretation suggests that the use of an orthonormal transformation matrix leads to performance depending solely on the subspace spanned by the column vectors of the matrix but not on the matrix itself. This is verified by simulations, and the numerical examples demonstrate the efficacy of the proposed algorithm.

Original language	English
Title of host publication	Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages	422-426
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2007.4487244
Publication status	Published - 2007 Dec 1
Externally published	Yes
Event	41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States Duration: 2007 Nov 4 → 2007 Nov 7

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/Territory	United States
City	Pacific Grove, CA
Period	07/11/4 → 07/11/7

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2007.4487244

Cite this

Yukawa, M., De Lamare, R. C., & Yamada, I. (2007). Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method. In Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC (pp. 422-426). Article 4487244 (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2007.4487244

Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method. / Yukawa, Masahiro; De Lamare, Rodrigo C.; Yamada, Isao.
Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC. 2007. p. 422-426 4487244 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yukawa, M, De Lamare, RC & Yamada, I 2007, Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method. in Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC., 4487244, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 422-426, 41st Asilomar Conference on Signals, Systems and Computers, ACSSC, Pacific Grove, CA, United States, 07/11/4. https://doi.org/10.1109/ACSSC.2007.4487244

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abstract = "In this paper, we propose a novel reduced-rank adaptive filtering algorithm based on set-theoretic adaptive filtering. We discuss the orthonormality of the transformation (rank-reduction) matrix. We present, under the assumption that the transformation matrix has an orthonormal structure, an interpretation of the proposed algorithm in the original (fullsize) vector space. The interpretation suggests that the use of an orthonormal transformation matrix leads to performance depending solely on the subspace spanned by the column vectors of the matrix but not on the matrix itself. This is verified by simulations, and the numerical examples demonstrate the efficacy of the proposed algorithm.",

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Set-theoretic reduced-rank adaptive filtering by adaptive projected subgradient method

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