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Postnonlinear overcomplete blind source separation using sparse sources

Theis, Fabian J. and Amari, S. (2004) Postnonlinear overcomplete blind source separation using sparse sources. In: Puntonet, Carlos G., (ed.) Independent component analysis and blind signal separation: fifth international conference, ICA 2004, Granada, Spain, September 22 - 24, 2004; proceedings. Lecture Notes in Computer Science, 3195. Springer, Berlin, pp. 718-725. ISBN 3-540-23056-4, 978-3-540-23056-4.

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Other URL: http://homepages.uni-regensburg.de/~thf11669/publications/theis04pnlSCA_ICA04.pdf


Abstract

We present an approach for blindly decomposing an observed random vector x into As where f is a diagonal function i.e. f=f_1 x ... x f_m with one-dimensional functions f_i and A an (m x n)-matrix. This postnonlinear model is allowed to be overcomplete, which means that less observations than sources (m$\lt$n) are given. In contrast to Independent Component Analysis (ICA) we do not assume the ...

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Export bibliographical data

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
Identification Number:
ValueType
10.1007/978-3-540-30110-3_91DOI
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:29 Sep 2010 09:35
Item ID:1606
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