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Dimension selection with independent component analysis and its application to prediction

Koch, I (New South Wales)
Friday 27 June 2008, 09:20-09:40

Seminar Room 1, Newton Institute


We consider the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. We review current methods, and propose a dimension selector based on Independent Component Analysis which finds the most non-Gaussian lower-dimensional directions in the data. A criterion for choosing the optimal dimension is based on bias-adjusted skewness and kurtosis. We show how this dimension selector can be applied in supervised learning with independent components, both in a regression and classification framework.


[pdf ]




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