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New Developments in Psychometrics pp 45–56Cite as

Relationships among Various Kinds of Eigenvalue and Singular Value Decompositions

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Summary

Eigenvalue decomposition (EVD) and/or singular value decomposition (SVD) play important roles in many multivariate data analysis techniques as computational tools for dimension reduction. A variety of EVD and SVD have been developed to deal with specific kinds of dimension reduction problems. This paper explicates various relationships among those decompositions with the prospect of exploiting them in practical applications of multivariate analysis.

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Authors and Affiliations

  1. McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, UK

    Yoshio Takane

Authors
  1. Yoshio Takane
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Editor information

H. Yanai A. Okada K. Shigemasu Y. Kano J. J. Meulman

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© 2003 Springer Japan

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Takane, Y. (2003). Relationships among Various Kinds of Eigenvalue and Singular Value Decompositions. In: Yanai, H., Okada, A., Shigemasu, K., Kano, Y., Meulman, J.J. (eds) New Developments in Psychometrics. Springer, Tokyo. https://doi.org/10.1007/978-4-431-66996-8_4

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  • DOI: https://doi.org/10.1007/978-4-431-66996-8_4

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-66998-2

  • Online ISBN: 978-4-431-66996-8

  • eBook Packages: Springer Book Archive

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