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Spatiotemporal Data Analysis$
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Gidon Eshel

Print publication date: 2011

Print ISBN-13: 9780691128917

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691128917.001.0001

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The Algebraic Operation of SVD

The Algebraic Operation of SVD

Chapter:
(p.75) Five The Algebraic Operation of SVD
Source:
Spatiotemporal Data Analysis
Author(s):

Gidon Eshel

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691128917.003.0005

Chapter 4 discussed the eigenvalue/eigenvector diagonalization of a matrix. Perhaps the biggest problem for this to be very useful in data analysis is the restriction to square matrices. It has already been emphasized time and again that data matrices, unlike dynamical operators, are rarely square. The algebraic operation of the singular value decomposition (SVD) is the answer. Note the distinction between the data analysis method widely known as SVD and the actual algebraic machinery. The former uses the latter, but is not the latter. This chapter describes the method. Following the introduction to SVD, it provides some examples and applications.

Keywords:   data analysis, data matrices, singular value decomposition, SVD, square matrices

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