2004 GeneralizedLowRankApproximation

• (Ye, 2004) ⇒ Jieping Ye. (2004). “Generalized Low Rank Approximations of Matrices.” In: Proceedings of the twenty-first International Conference on Machine learning. ISBN:1-58113-838-5 doi:10.1145/1015330.1015347

Subject Headings: Low Rank Approximation Task.

Cited By

• http://scholar.google.com/scholar?q=%222004%22+Generalized+Low+Rank+Approximations+of+Matrices
• http://dl.acm.org/citation.cfm?id=1015330.1015347&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 GeneralizedLowRankApproximation	Jieping Ye			Generalized Low Rank Approximations of Matrices				10.1145/1015330.1015347		2004