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Graphical Representation of Data Using Principal Components

  • I. T. Jolliffe
Part of the Springer Series in Statistics book series (SSS)

Abstract

The main objective of a PCA is to reduce the dimensionality of a set of data. This is particularly advantageous if a set of data with many variables lies, in reality, close to a two-dimensional subspace (plane). In this case the data can be plotted with respect to these two dimensions, thus giving a straightforward visual representation of what the data look like, instead of having a large mass of numbers to digest. If the data fall close to a three-dimensional subspace it is still possible, with a little effort, to gain a good visual impression of the data, especially if a computer is available with interactive graphics. Even with slightly more dimensions it is possible, with some degree of ingenuity, to get a ‘picture’ of the data—see, for example, Chapters 10–12 (by Tukey and Tukey) in Barnett (1981)—although we shall concentrate almost entirely on two-dimensional representations in the present chapter.

Keywords

Singular Value Decomposition Correspondence Analysis Minimum Span Tree Mahalanobis Distance Greylag Goose 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • I. T. Jolliffe
    • 1
  1. 1.Mathematical InstituteUniversity of KentKentEngland

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