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How to generalize Geometric ICA to higher dimensions

Theis, Fabian J. and Lang, Elmar (2002) How to generalize Geometric ICA to higher dimensions. In: Verleysen, Michel, (ed.) Proceedings / 10th European Symposium on Artificial Neural Networks, ESANN'2002: Bruges, Belgium, April 24 - 25 - 26, 2002. d-side, Evere, Belgium, pp. 205-211. ISBN 2-930307-02-1.

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Other URL: http://homepages.uni-regensburg.de/~thf11669/publications/theis02highdimquad_ESANN02.pdf


The geometric approach to ICA, proposed by Puntonet and Prieto, has one major drawback --- an exponentially rising number of samples and convergence times with increasing dimensiononality --- thus basically restricting geometric ICA to low-dimensional cases. We propose to apply overcomplete ICA to geometric ICA to reduce high-dimensional problems to lower-dimensional ones, thus generalizing geometric ICA to higher dimensions.

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Item type:Book section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Projects:Graduiertenkolleg Nichtlinearität und Nichtgleichgewicht
Dewey Decimal Classification:500 Science > 530 Physics
500 Science > 570 Life sciences
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:1557
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