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.

PDF
(252kB) - Repository staff only

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

Alternative links to fulltext:Publisher

Details

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:	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

Export bibliographical data

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 ...

Owner only: item control page

Download Statistics

More literature (via CORE)

Details

Export bibliographical data

Abstract

Abstract

Download Statistics

Downloads

More literature (via CORE)

University Library