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Robust independent component analysis

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3 Author(s)
Baloch, S.H. ; Dept of Electr. Comput. & Eng., North Carolina State Univ., Raleigh, NC ; Krim, H. ; Genton, M.G.

Independent component analysis (ICA) attempts to separate independent components present in the mixture signals. Several criteria have been suggested for ICA in the past, including kurtosis and negentropy. Kurtosis suffers from a drawback of being outlier sensitive. As a remedy, we propose robust ICA (RICA), which employs appropriate robust estimators. In this paper, we compare the robustness properties of RICA with kurtosis- and negentropy-based ICA. Since robust estimators are insensitive to outliers in contrast to maximum likelihood estimates (MLE), we demonstrate that in the presence of outliers, RICA works better than kurtosis- and negentropy-based ICA

Published in:

Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on

Date of Conference:

17-20 July 2005

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