Abstract
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 overcomplete ICA (i.e. more source signals than observed signals (mixtures)). © Springer-Verlag 2004.
Cite
CITATION STYLE
Meinecke, F. C., Harmeling, S., & Müller, K. R. (2004). Robust ICA for super-Gaussian sources. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 217–224. https://doi.org/10.1007/978-3-540-30110-3_28
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