Some Comments on Singular Value Decomposition Analysis

Steve Cherry Department of Mathematical Sciences, Montana State University, Bozeman, Montana

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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.

Corresponding author address: Dr. Steve Cherry, Dept. of Mathematical Sciences, Montana State University, Bozeman, MT 59717-0240. E-mail: imsgschemath.montana.edu

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.

Corresponding author address: Dr. Steve Cherry, Dept. of Mathematical Sciences, Montana State University, Bozeman, MT 59717-0240. E-mail: imsgschemath.montana.edu

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