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 proceedingConference 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 languageEnglish
Title of host publicationConference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages422-426
Number of pages5
DOIs
Publication statusPublished - 2007 Dec 1
Event41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States
Duration: 2007 Nov 42007 Nov 7

Publication series

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

Other

Other41st Asilomar Conference on Signals, Systems and Computers, ACSSC
CountryUnited States
CityPacific Grove, CA
Period07/11/407/11/7

    Fingerprint

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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). [4487244] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2007.4487244