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Clustering of signals using incomplete independent component analysis

Keck, I. R. and Lang, Elmar and Nassabay, S. and Puntonet, C. G. (2005) Clustering of signals using incomplete independent component analysis. In: Cabestany, Joan, (ed.) Computational intelligence and bioinspired systems: 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Barcelona, Spain, June 8 - 10, 2005; proceedings. Lecture notes in computer science, 3512. Springer, Berlin. ISBN 3-540-26208-3.

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Other URL: http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-12-72397-0,00.html


In this paper we propose a new algorithm for the clustering of signals using incomplete independent component analysis (ICA). In the first step we apply the ICA to the dataset without dimension reduction, in the second step we reduce the dimension of the data to find clusters of independent components that are similar in their entries in the mixture matrix found by the ICA. We ...


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Item Type:Book Section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Projects:BMBF Projekt ModKog
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
Deposited On:20 Mar 2007
Last Modified:30 Sep 2010 08:26
Item ID:1636
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