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A novel dimension reduction procedure for searching non-Gaussian subspaces

  • 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

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Metadaten
Author:Motoaki Kawanabe, Gilles BlanchardGND, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller
URL:http://www.springerlink.com/content/105633/
DOI:https://doi.org/10.1007/11679363_19
ISSN:0302-9743
Document Type:Article
Language:English
Year of first Publication:2006
Year of Completion:2006
Release Date:2017/03/24
Source:Lecture notes in computer science : independent component analysis and blind signal separation : proceedings. - ISSN 0302-9743. - 3889 (2006), S. 149 - 156
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer Review:Referiert
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik