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Blind source separation and sparse component analysis of overcomplete mixtures

Georgiev, P. ; Theis, Fabian J. ; Cichocki, A.



Abstract

We formulate conditions (k-SCA-conditions) under which we can represent a given (m x N)-matrix X (data set) uniquely (up to scaling and permutation) as a multiplication of (m x n) and (n x N) matrices A and S (often called mixing matrix or dictionary and source matrix, respectively), such that S is sparse of level n-m+k in sense that each column of S has at least n-m+k zero elements. We call this ...

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