PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Neurally plausible, non-combinatorial iterative independent process analysis
András Lorincz and Zoltan Szabo
Neurocomputing - Letters Volume 70, Number 7-9, pp. 1569-1573, 2007.

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Abstract

It has been shown recently that the identification of mixed hidden independent auto-regressive processes (independent process analysis, IPA), under certain conditions, can be free from combinatorial explosion. The key is that IPA can be reduced (i) to independent subspace analysis and then, via a novel decomposition technique called Separation Theorem, (ii) to independent component analysis. Here, we introduce an iterative scheme and its neural network representation that takes advantage of the reduction method and can accomplish the IPA task. Computer simulation illustrates the working of the algorithm.

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EPrint Type:Article
Additional Information:http://dx.doi.org/10.1016/j.neucom.2006.10.145
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9536
Deposited By:Zoltan Szabo
Deposited On:29 May 2012

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