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Orthogonalization Via Deflation: A Minimum Norm Approach for Low-Rank Approximations of a Matrix

โœ Scribed by Dax, Achiya

Book ID
118216920
Publisher
Society for Industrial and Applied Mathematics
Year
2008
Tongue
English
Weight
246 KB
Volume
30
Category
Article
ISSN
0895-4798

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A minimum norm approach for low-rank app
A minimum norm approach for low-rank approximations of a matrix
โœ Achiya Dax ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 367 KB

The problems of calculating a dominant eigenvector or a dominant pair of singular vectors, arise in several large scale matrix computations. In this paper we propose a minimum norm approach for solving these problems. Given a matrix, A, the new method computes a rankone matrix that is nearest to A,