Sparse blind identification and separation by using adaptive K-orthodrome clustering

Yoshikazu Washizawa, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

We propose a new algorithm for identifying a 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 unknown matrix S. We present a simple and efficient adaptive algorithm for such identification and illustrate its performance by estimation of the unknown mixing matrix A and source signals (sparse components) represented by rows of the matrix S. The main feature of the proposed algorithm is its adaptivity to changing (non-stationary) environment and robustness with respect to outliers that do not necessarily satisfy sparseness conditions.

Original languageEnglish
Pages (from-to)2321-2329
Number of pages9
JournalNeurocomputing
Volume71
Issue number10-12
DOIs
Publication statusPublished - Jun 2008
Externally publishedYes

Keywords

  • Adaptive learning for blind identification
  • K-plane clustering
  • Overcomplete blind source separation
  • Sparse representation

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