Singular Value Decomposition

Simo Puntanen (), George P. H. Styan () and Jarkko Isotalo ()
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Simo Puntanen: University of Tampere, School of Information Sciences
George P. H. Styan: McGill University, Department of Mathematics & Statistics
Jarkko Isotalo: University of Tampere, School of Information Sciences

Chapter Chapter 19 in Matrix Tricks for Linear Statistical Models, 2011, pp 391-414 from Springer

Abstract: Abstract While the eigenvalue decomposition $${\bf A} = \bf T{\bf \Lambda}T^{\prime},$$ say, concerns only symmetric matrices, the singular value decomposition (SVD) $${\bf A} = \bf U{\bf \Delta}V^{\prime},$$ say, concerns any n × m matrix. In this chapter we illustrate the usefulness of the SVD, particularly from the statistical point of view. Surprisingly many statistical methods have connections to the SVD.

Keywords: Singular Value Decomposition; Canonical Correlation; Singular Vector; Full Column Rank; Orthogonal Distance (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-10473-2_20

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DOI: 10.1007/978-3-642-10473-2_20

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