Sparse Component Analysis by Improved Basis Pursuit Method
نویسندگان
چکیده
We give conditions under which we can solve precisely the Blind Source Separation problem (BSS) in the underdetermined case (less sensors than sources) uniquely, up to permutation and scaling of sources. Under this conditions, which include information about sparseness of the sources (and hence we call the problem sparse component analysis (SCA)), we can 1) identify the mixing matrix (up to scaling and permutation) and 2) estimate the sources. We present an algorithm for estimation of the mixing matrix, We present as well an algorithm for SCA, which improves the basis pursuit method of Donoho and Chen (when the mixing matrix is known or estimated), and we illustrate our method with examples.
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