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Some Comments on Singular Value Decomposition Analysis

Some Comments on Singular Value Decomposition Analysis The singular value decomposition analysis (SVD) method is discussed in the context of the simultaneous orthogonal rotation of two matrices. It is demonstrated that the singular vectors are rotated EOFs and the SVD expansion coefficients are rotated sets of principal component expansion coefficients. This way of thinking about SVD aids in the interpretation of results and provides guidance as to when and how to use SVD. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Some Comments on Singular Value Decomposition Analysis

Journal of Climate , Volume 10 (7) – Jan 26, 1996

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Publisher
American Meteorological Society
Copyright
Copyright © 1996 American Meteorological Society
ISSN
1520-0442
DOI
10.1175/1520-0442(1997)010<1759:SCOSVD>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

The singular value decomposition analysis (SVD) method is discussed in the context of the simultaneous orthogonal rotation of two matrices. It is demonstrated that the singular vectors are rotated EOFs and the SVD expansion coefficients are rotated sets of principal component expansion coefficients. This way of thinking about SVD aids in the interpretation of results and provides guidance as to when and how to use SVD.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Jan 26, 1996

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