On continuous partial singular value decomposition algorithms

Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimiza...

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Bibliographic Details
Published in2009 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 840 - 843
Main Author Hasan, M.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2009
Subjects
Application software
Approximation algorithms
Computer vision
Data mining
Differential equations
Image coding
Image retrieval
matrix approximation
Matrix decomposition
Optimization methods
principal singular subspace
Singular value decomposition
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ISBN1424438276
9781424438273
ISSN0271-4302
DOI10.1109/ISCAS.2009.5117887

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Summary:Low-rank matrix approximation arises in various applications. It is an effective tool in alleviating the memory and computational burdens in many algorithmic development and implementation. In this paper, two methods for computing low rank approximation are proposed and derived by utilizing optimization techniques of unconstrained merit functions. The proposed techniques led to computing low-rank matrix approximation by solving nonlinear matrix differential equations. Numerical experiments illustrate the theoretical results.
ISBN:1424438276
9781424438273
ISSN:0271-4302
DOI:10.1109/ISCAS.2009.5117887