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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., & Müller, K. R. (2006). A novel dimension reduction procedure for searching non-Gaussian subspaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 149–156). https://doi.org/10.1007/11679363_19
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