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.
This work is supported by the NSF grants NSF-CCF 0635129 and NSF-IIS 0612076.
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Anderson, M., Adalı, T. (2010). A General Approach for Robustification of ICA Algorithms. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_37
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DOI: https://doi.org/10.1007/978-3-642-15995-4_37
Publisher Name: Springer, Berlin, Heidelberg
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