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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 Svd Analysis of Two Fields

The Svd Analysis of Two Fields

Chapter:
(p.261) Twelve The Svd Analysis of Two Fields
Source:
Spatiotemporal Data Analysis
Author(s):

Gidon Eshel

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691128917.003.0012

Chapter 11 discussed one of the many methods available for simultaneously analyzing more than one data set. While powerful and useful (especially for unveiling favored state evolution pathways), the extended empirical orthogonal function (EEOF) procedure has some important limitations. Notably, because the state dimensions rapidly expand as state vectors are appended end-to-end, EEOF analysis may not always be numerically tractable. For analyzing two data sets, taking note of their cross-covariance but not explicitly of individual sets’ covariance, the singular value decomposition (SVD) method is the most natural. This chapter discusses SVD analysis of two fields. SVD analysis can be thought of as a generalization of EOF analysis to two data sets that are believed to be related.

Keywords:   singular value decomposition, SVD analysis, data analysis, empirical orthogonal functions, EOF analysis

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