On-line K-plane clustering learning algorithm for sparse comopnent analysis

Yoshikazu Washizawa, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

34 Citations (Scopus)

Abstract

In this paper we propose a new algorithm for identifying mixing (basis) matrix A knowing only sensor (data) matrix X for linear model X = AS + E, under some weak or relaxed conditions, expressed in terms of sparsity of latent (hidden) components represented by the matrix S. We present a simple and efficient on-line algorithm for such identification and illustrate its performance by estimation of unknown matrix A and source signals S. The main feature of the proposed algorithm is its adaptivity to changing environment and robustness in respect to noise and outliers that do not satisfy sparseness conditions.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesV681-V684
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period14/05/0619/05/06

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