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On model identifiability in analytic postnonlinear ICA

Theis, Fabian J. and Gruber, P. (2005) On model identifiability in analytic postnonlinear ICA. Neurocomputing 64, pp. 223-234.

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Abstract

An important aspect of successfully analyzing data with blind source separation is to know the indeterminacies of the problem, that is how the separating model is related to the original mixing model. If linear independent component analysis (ICA) is used, it is well-known that the mixing matrix can be found in principle, but for more general settings not many results exist. In this work, only ...

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Item Type:Article
Date:2005
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang > Arbeitsgruppe Dr. Fabian Theis
Projects:Graduiertenkolleg Nichtlinearität und Nichtgleichgewicht
Identification Number:
ValueType
10.1016/j.neucom.2004.11.015DOI
Subjects: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
Owner: Redakteur Physik
Deposited On:20 Mar 2007
Last Modified:04 Oct 2010 07:37
Item ID:1618
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