PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Undercomplete blind subspace deconvolution
Zoltan Szabo, Barnabas Poczos and András Lorincz
Journal of Machine Learning Research Volume 8, pp. 1063-1095, 2007.

Abstract

We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated 'high dimensional' ISA problem can be handled by a recent technique called joint f-decorrelation (JFD). Similar decorrelation methods have been used previously for kernel independent component analysis (kernel-ICA). More precisely, the kernel canonical correlation (KCCA) technique is a member of this family, and, as is shown in this paper, the kernel generalized variance (KGV) method can also be seen as a decorrelation method in the feature space. These kernel based algorithms will be adapted to the ISA task. In the numerical examples, we (i) examine how efficiently the emerging higher dimensional ISA tasks can be tackled, and (ii) explore the working and advantages of the derived kernel-ISA methods.

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EPrint Type:Article
Additional Information:http://www.jmlr.org/papers/volume8/szabo07a/szabo07a.pdf
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8369
Deposited By:Zoltan Szabo
Deposited On:01 December 2011