F.J. Theis and E.W. Lang (Germany)
geometric independent component analysis, blind source separation, overcomplete independent component analysis
We discuss overcomplete blind source separation (BSS), that is separation with more sources than sensors. Finding the mixing matrix solves the linear quadratic BSS prob lem; in overcomplete BSS however, it is then still not clear and not even unique how to get the sources from the mix tures and the mixing matrix. We therefore follow Bofill and Zibulevsky in [5] and many others and take a two-step approach to overcomplete BSS: In the first so called blind mixing model recovery (BMMR) step, the mixing model has to be reconstructed from the mixtures - in the linear case this would mean finding the mixing matrix. Then, in the blind source recovery (BSR) step, the sources have to be reconstructed given the mixing matrix and the mixtures. We furthermore introduce some notation and sayings and describe the usual BSR step in order to enable forthcom ing overcomplete BSS papers to concentrate on one of the two steps, mainly on the BMMR step. Finally, we prove that the shortest-path algorithm as proposed by Bofill and Zibulevsky in [5] indeed solves the maximum-likelihood conditions in the BSR step.
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