PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Real and complex independent subspace analysis by generalized variance
Zoltan Szabo and András Lorincz
In: ICA Research Network International Workshop (ICARN), 18-19 Sep 2006, Liverpool, U.K..

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Abstract

Here, we address the problem of Independent Subspace Analysis (ISA). We develop a technique that (i) builds upon joint decorrelation for a set of functions, (ii) can be related to kernel based techniques, (iii) can be interpreted as a self-adjusting, self-grouping neural network solution, (iv) can be used both for real and for complex problems, and (v) can be a first step towards large scale problems. Our numerical examples extend to a few 100 dimensional ISA tasks.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8366
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

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