A novel dimension reduction procedure for searching non-Gaussian subspaces

Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir Spokoiny, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIndependent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
Pages149-156
Number of pages8
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, United States
Duration: 2006 Mar 52006 Mar 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3889 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
CountryUnited States
CityCharleston, SC
Period06/3/506/3/8

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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