Undercomplete Blind Subspace Deconvolution

Details Undercomplete Blind Subspace Deconvolution Undercomplete Blind Subspace Deconvolution

2007-01-07

arxiv.org/abs/math/0701210v3

Zoltan Szabo, Barnabas Poczos, Andras Lorincz: Undercomplete Blind Subspace Deconvolution. Journal of Machine Learning Researc

Mathematics

Statistics Theory

Final version, appeared in Journal of Machine Learning Research

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Undercomplete Blind Subspace Deconvolution does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Undercomplete Blind Subspace Deconvolution, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Undercomplete Blind Subspace Deconvolution will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-370236