  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.
