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Robust ICA for Super-Gaussian Sources

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Meinecke, F., Harmeling, S., & Müller, K.-R. (2004). Robust ICA for Super-Gaussian Sources. Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004), 217-224.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D7A9-1
Zusammenfassung
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 over-complete ICA (i.e. more source signals than observed signals (mixtures)).