TY  - JOUR
A1  - Kawanabe, Motoaki
A1  - Blanchard, Gilles
A1  - Sugiyama, Masashi
A1  - Spokoiny, Vladimir G.
A1  - Müller, Klaus-Robert
T1  - A novel dimension reduction procedure for searching non-Gaussian subspaces
N2  - 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 semi-parametric 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
Y1  - 2006
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/12324
UR  - http://www.springerlink.com/content/105633/
SN  - 0302-9743
ER  -