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

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

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 proceeding › Conference contribution

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 language	English
Title of host publication	Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages	131-138
Number of pages	8
Publication status	Published - 2005
Externally published	Yes
Event	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: 2005 Dec 5 → 2005 Dec 8

Publication series

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

Other

Other	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
Country/Territory	Canada
City	Vancouver, BC
Period	05/12/5 → 05/12/8

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

Non-Gaussian component analysis : A semi-parametric framework for linear dimension reduction. / Blanchard, G.; Sugiyama, M.; Kawanabe, M. et al.

Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference. 2005. p. 131-138 (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Blanchard, G, Sugiyama, M, Kawanabe, M, Spokoiny, V & Müller, KR 2005, Non-Gaussian component analysis: A semi-parametric framework for linear dimension reduction. in Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference. Advances in Neural Information Processing Systems, pp. 131-138, 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005, Vancouver, BC, Canada, 05/12/5.

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AU - Müller, K. R.

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AB - 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.

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M3 - Conference contribution

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BT - Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference

Y2 - 5 December 2005 through 8 December 2005

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

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