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- I. Schießl,
H. Schöner, M. Stetter, A. Dima, and K. Obermayer. Regularized Second
Order Source Separation.
.
In P. Pajunen and J. Karhunen, editors, Int. Workshop on Independent
Component Analysis and Blind Signal Separation, volume 2, pages
111-116, 2000.
(FTP Gzipped PostScript, 5 pages, 351 kb)
In the separation task of linear mixtures from real experiments the
dependencies of the original sources often make "classical"
independent component analysis (ICA) algorithms fail. One way to overcome
this drawback is the introduction of additional knowledge we have about the
mixing process. We introduce a regularization term to the cost function of
multishift extended spatial decorrelation (multishift ESD) that punishes the
deviation of the time course of the estimated sources from a assumed time
course during an experiment. In the case of optical imaging such knowledge
can be achieved from the metabolic response of signals to the stimulus onset.
We show how the regularization term improves the separation result at
different noise levels. The simulations were run on a artificial toy dataset
and one dataset that contains prototype signals from a real optical imaging
experiment.
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