PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A novel dimension reduction procedure for searching non-gaussian subspaces.
Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir Spokoiny and Klaus-Robert Müller
In: ICA 2006, 5-8 march 2006, Charleston, USA.


In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new linear method to identify the non-Gaussian subspace. Our method NGCA (Non-Gaussian Component Analysis) is based on a very general semiparametric framework and has a theoretical guarantee that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. NGCA can be used not only as preprocessing for ICA, but also for extracting and visualizing more general structures like clusters. A numerical study demonstrates the usefulness of our method.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2535
Deposited By:Gilles Blanchard
Deposited On:22 November 2006