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

Authors:
Yoshikazu Washizawa;Andrzej Cichocki
Affiliations:
Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, 2-1, Hirosawa, Wako-shi, Saitama 351-0198, Japan;Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, 2-1, Hirosawa, Wako-shi, Saitama 351-0198, Japan
Venue:
Neurocomputing
Year:
2008

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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.