Theis, Fabian J. and Lang, Elmar and Puntonet, Carlos G. (2004) A Geometric Algorithm for Overcomplete Linear ICA. Neurocomputing 56, pp. 381-398.
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Other URL: http://homepages.uni-regensburg.de/~thf11669/publications/theis04overcomplete_neurocomp.pdf
Abstract
We generalize geometric algorithms to overcomplete cases with more sources than sensors. With geometric ICA we get an efficient method for the matrix-recovery step in the framework of a two-step approach to the source separation problem. The second step - source-recovery - uses a maximum-likelihood approach. There we prove that the shortest-path algorithm as proposed by Bofill and Zibulevsky indeed solves the maximum-likelihood conditions.
| Item Type: | Article | ||||
|---|---|---|---|---|---|
| Institutions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang > Arbeitsgruppe Dr. Fabian Theis | ||||
| Projects: | Graduiertenkolleg Nichtlinearität und Nichtgleichgewicht | ||||
| Identification Number: |
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| Subjects: | 500 Science > 530 Physics 500 Science > 570 Life sciences | ||||
| Status: | Published | ||||
| Refereed: | Yes, this version has been refereed | ||||
| Created at the University of Regensburg: | Yes | ||||
| Owner: | Redakteur Physik | ||||
| Deposited On: | 20 Mar 2007 | ||||
| Last Modified: | 04 Oct 2010 09:47 | ||||
| Item ID: | 1585 |
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