Go to content
UR Home

Blind source separation and sparse component analysis of overcomplete mixtures

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


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 ...


Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de
0941 943 -4239 or -69394

Dissertations: dissertationen@ur.de
0941 943 -3904

Research data: datahub@ur.de
0941 943 -5707

Contact persons