  We present a novel solution technique for the blind subspace deconvolution
(BSSD) problem, where temporal convolution of multidimensional hidden
independent components is observed and the task is to uncover the hidden
components using the observation only. We carry out this task for the
undercomplete case (uBSSD): we reduce the original uBSSD task via linear
prediction to independent subspace analysis (ISA), which we can solve. As it
has been shown recently, applying temporal concatenation can also reduce uBSSD
to ISA, but the associated ISA problem can easily become `high dimensional'
[1]. The new reduction method circumvents this dimensionality problem. We
perform detailed studies on the efficiency of the proposed technique by means
of numerical simulations. We have found several advantages: our method can
achieve high quality estimations for smaller number of samples and it can cope
with deeper temporal convolutions.
