A general approach for robustification of ICA algorithms

Authors:
Matthew Anderson;Tülay Adali
Affiliations:
Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

This paper presents a general and robust approach to mitigating impact of outliers in independent component analysis applications. The approach detects and removes outlier samples from the dataset and has minimal impact on the overall performance when the dataset is free of outliers. It also has minimal computational burdens, is simply parameterized, and readily implemented. Significant gains in performance is shown for algorithms when outliers are present.