Gradient-Type Algorithms for Partial Singular Value Decomposition

Raziel Haimi Cohen; Arnon Cohen

doi:10.1109/TPAMI.1987.4767879

Gradient-Type Algorithms for Partial Singular Value Decomposition

Raziel Haimi Cohen, Arnon Cohen

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

It is often desirable to calculate only a few terms of the SVD expansion of a matrix, corresponding to the largest or smallest singular values. Two algorithms, based on gradient and conjugate gradient search, are proposed for this purpose. SVD is computed term by term in a decreasing or increasing order of singular values. The algorithms are simple to implement and are especially advantageous with large matrices.

Original language	English
Pages (from-to)	137-142
Number of pages	6
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	PAMI-9
Issue number	1
DOIs	https://doi.org/10.1109/TPAMI.1987.4767879
State	Published - 1 Jan 1987

Keywords

Conjugate gradient
Rayleigh quotient
gradient search
partial singular
value decomposition

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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10.1109/TPAMI.1987.4767879

Cite this

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Gradient-Type Algorithms for Partial Singular Value Decomposition

Abstract

Keywords

ASJC Scopus subject areas

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