Generalized low rank approximations of matrices

Jieping Ye

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

86 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 languageEnglish (US)
Title of host publicationProceedings, Twenty-First International Conference on Machine Learning, ICML 2004
EditorsR. Greiner, D. Schuurmans
Pages887-894
Number of pages8
StatePublished - Dec 1 2004
EventProceedings, Twenty-First International Conference on Machine Learning, ICML 2004 - Banff, Alta, Canada
Duration: Jul 4 2004Jul 8 2004

Publication series

NameProceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Other

OtherProceedings, Twenty-First International Conference on Machine Learning, ICML 2004
Country/TerritoryCanada
CityBanff, Alta
Period7/4/047/8/04

Keywords

  • Classification
  • Matrix approximation
  • Singular Value Decomposition

ASJC Scopus subject areas

  • Engineering(all)

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