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.
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