Non-Gaussian component analysis: A semi-parametric framework for linear dimension reduction

G. Blanchard, M. Sugiyama, M. Kawanabe, V. Spokoiny, K. R. Müller

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

5 Citations (Scopus)

Abstract

We propose a new linear method for dimension reduction to identify non- Gaussian components in high dimensional data. Our method, NGCA (non-Gaussian component analysis), uses a very general semi-parametric framework. In contrast to existing projection methods we define what is uninteresting (Gaussian): by projecting out uninterestingness, we can estimate the relevant non-Gaussian subspace. We show that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. Once NGCA components are identified and extracted, various tasks can be applied in the data analysis process, like data visualization, clustering, denoising or classification. A numerical study demonstrates the usefulness of our method.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages131-138
Number of pages8
Publication statusPublished - 2005
Externally publishedYes
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: 2005 Dec 52005 Dec 8

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CountryCanada
CityVancouver, BC
Period05/12/505/12/8

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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