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Robust overcomplete matrix recovery for sparse sources using a generalized Hough transform

Theis, Fabian J. and Georgiev, P. and Cichocki, A. (2004) Robust overcomplete matrix recovery for sparse sources using a generalized Hough transform. In: Verleysen, Michel, (ed.) Proceedings / 12th European Symposium on Artificial Neural Networks, ESANN 2004: Bruges, Belgium, April 28 - 30, 2004. d-side, Evere, Belgium, pp. 343-348. ISBN 2-930307-04-8.

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Other URL: http://homepages.uni-regensburg.de/~thf11669/publications/theis04houghSCA_ESANN04.pdf


Abstract

We propose an algorithm for recovering the matrix A in X = AS where X is a random vector of lower dimension than S. S is assumed to be sparse in the sense that S has less nonzero elements than the dimension of X at any given time instant. In contrast to previous approaches, the computational time of the presented algorithm is linear in the sample number and independent of source dimension, and ...

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Item Type:Book Section
Date:2004
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
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:15 Oct 2010 06:03
Item ID:1608
Owner Only: item control page
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