Undercomplete blind subspace deconvolution

Zoltán Szabó, Barnabás Póczos, András Lorincz

Research output: Contribution to journalArticle

34 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1063-1095
Number of pages33
JournalJournal of Machine Learning Research
Volume8
Publication statusPublished - May 1 2007

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Keywords

  • Independent subspace analysis
  • Joint decorrelation
  • Kernel methods
  • Undercomplete blind subspace deconvolution

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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