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Connecting geometric independent component analysis to unsupervised learning algorithms

Theis, Fabian J. and Gruber, Peter and Puntonet, Carlos G. and Lang, Elmar (2004) Connecting geometric independent component analysis to unsupervised learning algorithms. In: Fourth International ICSC Symposium on Engineering of Intelligent Systems, EIS 2004: University of Madeira, Funchal, Portugal, February 29 - March 2, 2004; proceedings. ICSC Interdisciplinary Research Canada, Millet, Alberta. ISBN 3-906454-35-5.

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Other URL: http://homepages.uni-regensburg.de/~thf11669/publications/theis04SOMNGICA_EIS04.pdf


Abstract

The goal of independent component analysis (ICA) lies in transforming a mixed random vector in order to render it as independent as possible. This paper shows how to use adaptive learning and clustering algorithms to approximate mixture space densities thus learning the mixing model. Here, a linear square-model is assumed, and as learning algorithm either a self-organizing map (SOM) or a neural ...

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Export bibliographical data

Item Type:Book Section
Date:2004
Additional information (public):1 CD-ROM (enth. Proceedings) + 1 Buch (enth. Abstracts)
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:14 Oct 2010 12:19
Item ID:1597
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