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Robust ICA for super-gaussian sources

: Meinecke, F.C.; Harmeling, S.; Müller, K.-R.

Puntonet, C.G.:
Independent component analysis and blind signal separation : Fifth international conference, ICA 2004, Granada, Spain, September 22 - 24, 2004 ; proceedings
Berlin: Springer, 2004 (Lecture Notes in Computer Science 3195)
ISBN: 3-540-23056-4
ISSN: 0302-9743
International Conference on Independent Component Analysis and Blind Signal Seperation (ICA) <5, 2004, Granada>
Conference Paper
Fraunhofer FIRST ()

Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals (mixtures)).