Multiple sparse component analysis based on subspace selective search algorithm

Sparse component analysis (SCA) is an approach for linear matrix factorization in an instantaneous mixing system when the number of sensors is fewer than the number of sources. SCA assumes that matrix source (S) contains as many zeros as possible. According to the Georgiev's proof, under some nonstrict conditions on sparsity of the sources, called k-sparse component analysis (k-SCA), we are able to estimate both mixing system (A) and sparse sources (S) uniquely. This paper studies the problem of underdetermind blind identification (UBI) in order to estimate the mixing matrix A based on subspace clustering scheme with k-SCA assumptions. Most k-SCA based algorithms have been designed when there are only at most k = m - 1 active sources in each time instant, where m is the number of sensors. First, we address the issue of multiple active sources i.e. when there are L active sources (0 ≤ L ≤ m - 1). Second, we propose an algorithm for joint or continuous subspace clustering and estimating of the channels in order to design an online scenario to estimate the mixing matrix columns and detect the number of sources as the mixture vectors are received sequentially. Third, our proposed algorithm “subspace selective search” (S
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) deals with the outliers within subspace clustering process and improve the accuracy of channel estimation. Numerical simulations are reported to confirm the advantages of our UBI method.