Generalized low rank approximations of matrices

Jieping Ye

Generalized low rank approximations of matrices

Jieping Ye

Biodesign Institute

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

35 Scopus citations

Abstract

We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank approximations are on a sequence of matrices. Unlike the problem of low rank approximations of a single matrix, which was well studied in the past, the proposed algorithm in this paper does not admit a closed form solution in general. We did extensive experiments on face image data to evaluate the effectiveness of the proposed algorithm and compare the computed low rank approximations with those obtained from traditional Singular Value Decomposition based method.

Original language	English (US)
Title of host publication	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Editors	R. Greiner, D. Schuurmans
Pages	887-894
Number of pages	8
State	Published - Dec 1 2004
Event	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 - Banff, Alta, Canada Duration: Jul 4 2004 → Jul 8 2004

Publication series

Name	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Other

Other	Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Country/Territory	Canada
City	Banff, Alta
Period	7/4/04 → 7/8/04

Keywords

Classification
Matrix approximation
Singular Value Decomposition

ASJC Scopus subject areas

Engineering(all)

Cite this

Generalized low rank approximations of matrices. / Ye, Jieping.
Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. ed. / R. Greiner; D. Schuurmans. 2004. p. 887-894 (Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004).

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

Ye, J 2004, Generalized low rank approximations of matrices. in R Greiner & D Schuurmans (eds), Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, pp. 887-894, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, Banff, Alta, Canada, 7/4/04.

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AB - We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank approximations are on a sequence of matrices. Unlike the problem of low rank approximations of a single matrix, which was well studied in the past, the proposed algorithm in this paper does not admit a closed form solution in general. We did extensive experiments on face image data to evaluate the effectiveness of the proposed algorithm and compare the computed low rank approximations with those obtained from traditional Singular Value Decomposition based method.

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BT - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

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Generalized low rank approximations of matrices

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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