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- R. Vollgraf and
K. Obermayer. Multi Dimensional ICA to Separate Correlated Sources.
.
In Advances in Neural Information Processing Systems 14, pages
993-1000, Cambridge, Massachusetts, 2002. MIT Press.
(FTP Gzipped PostScript, 8 pages, 514 kb)
We present a new method for the blind separation of sources, which
do not fulfill the independence assumption. In contrast to standard methods
we consider groups of neighboring samples (``patches) within the observed
mixtures. First we extract independent features from the observed patches. It
turns out that the average dependencies between these features in different
sources is in general lower than the dependencies between the amplitudes of
different sources. We show that it might be the case that most of the
dependencies is carried by only a small number of features. Is this case -
provided these features can be identified by some heuristic - we project all
patches into the subspace which is orthogonal to the subspace spanned by the
``correlated features. Standard ICA is then performed on the elements of
the transformed patches (for which the independence assumption holds) and
robustly yields a good estimate of the mixing matrix.
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