Linear Geometric ICA: Fundamentals and Algorithms

DOI to cite this document:: 10.5283/epub.1516

Theis, Fabian J. ; Jung, Andreas ; Puntonet, Carlos G. ; Lang, Elmar W.

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Date of publication of this fulltext: 05 Aug 2009 13:30

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Item type:	Article
Journal or Publication Title:	Neural Computation
Publisher:	MIT Press
Volume:	15
Page Range:	pp. 419-439
Date:	2003
Institutions:	Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang Physics > Institute of Theroretical Physics > Chair Professor Richter > Group Klaus Richter
Projects (Historical):	Graduiertenkolleg Nichtlinearität und Nichtgleichgewicht
Dewey Decimal Classification:	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
Item ID:	1516

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Abstract

Geometric algorithms for linear independent component analysis (ICA) have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA was proposed first by Puntonet and Prieto (1995). We will reconsider geometric ICA in a theoretic framework showing that fixed points of geometric ICA fulfill a geometric ...

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