DMCA
Undercomplete blind subspace deconvolution (2007)
Cached
Download Links
| Venue: | JMLR |
| Citations: | 26 - 18 self |
BibTeX
@ARTICLE{Szabó07undercompleteblind,
author = {Zoltán Szabó and Barnabás Póczos and András Lőrincz and Eötvös Loránd},
title = {Undercomplete blind subspace deconvolution},
journal = {JMLR},
year = {2007},
pages = {1063--1095}
}
Share
 |
 |
 |
 |
||
OpenURL
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.
Keyphrases
undercomplete blind subspace deconvolution feature space decorrelation method blind source deconvolution independent subspace analysis recent technique temporal concatenation similar decorrelation method associated high dimensional isa problem numerical example kernel canonical correlation undercomplete bssd blind subspace deconvolution joint f-decorrelation isa task kernel independent component analysis dimensional isa task kernel-isa method
