@inproceedings{6654dbdb225c45b3acd8d79b681e7331,
title = "Robust independent component analysis",
abstract = "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.",
author = "Baloch, {Sajjad H.} and Hamid Krim and Genton, {Marc G.}",
year = "2005",
doi = "10.1109/ssp.2005.1628565",
language = "English (US)",
isbn = "0780394046",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "61--64",
booktitle = "2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts",
address = "United States",
note = "2005 IEEE/SP 13th Workshop on Statistical Signal Processing ; Conference date: 17-07-2005 Through 20-07-2005",
}