DocumentCode :
396766
Title :
SOMICA - an application of self-organizing maps to geometric independent component analysis
Author :
Theis, Fabian J. ; Puntonet, Carlos G. ; Lang, Elmar W.
Author_Institution :
Inst. of Biophys., Regensburg Univ., Germany
Volume :
2
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
1318
Abstract :
Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOM-ICA) to linear geometric ICA using self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.
Keywords :
blind source separation; independent component analysis; self-organising feature maps; SOMICA algorithm; blind source separation; geometric independent component analysis; linear geometric ICA; self-organizing map; Biophysics; Blind source separation; Computational efficiency; Covariance matrix; Independent component analysis; Self organizing feature maps; Source separation; Statistics; Symmetric matrices; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1223886
Filename :
1223886
Link To Document :
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