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Principal Components Analysis

  • Chapter
Multivariate Data Analysis

Part of the book series: Astrophysics and Space Science Library ((ASSL,volume 131))

Abstract

We have seen in Chapter 1 how the n × m data array which is to be analysed may be viewed immediately as a set of n row-vectors, or alternatively as a set of m column-vectors. PCA seeks the best, followed by successively less good, summarizations of this data. Cluster Analysis, as will be seen in Chapter 3, seeks groupings of the objects or attributes. By focussing attention on particular groupings, Cluster Analysis can furnish a more economic presentation of the data. PCA (and other techniques, as will be seen in a later chapter) has this same objective but a very different summarization of the data is aimed at.

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Examples from Astronomy

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Author information

Authors and Affiliations

  1. ST-ECF/European Southern Observatory, Munich, Germany

    Fionn Murtagh

  2. Space Science Department, ESTEC, Noordwijk, The Netherlands

    Fionn Murtagh

  3. Observatoire Astronomique, C.D.S., Strasbourg, France

    André Heck

Authors
  1. Fionn Murtagh
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  2. André Heck
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland

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Murtagh, F., Heck, A. (1987). Principal Components Analysis. In: Multivariate Data Analysis. Astrophysics and Space Science Library, vol 131. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3789-5_2

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  • DOI: https://doi.org/10.1007/978-94-009-3789-5_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-277-2426-7

  • Online ISBN: 978-94-009-3789-5

  • eBook Packages: Springer Book Archive

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