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Multiple discriminant analysis

In: Introduction to Biometry

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  • Pierre Jolicoeur

    (University of Montreal, Department of Biological Science)

Abstract

While multivariate differences between several groups of observations could be described by pairing these groups in all possible manners and by evaluating Mahalanobis’ generalized distance between their means (section 32.5), a much better performing method known as multiple discriminant analysis will be discussed in this chapter. Multiple discriminant analysis is also called canonical variate analysis, but the latter expression is less adequate because canonical variates may be used even when there is only one group of observations (section 30.8, chapter 31, chapter 34). Multiple discriminant analysis may be considered as a principal component analysis (chapter 31) in which the principal axes of between-groups variation are determined after within-groups variation has been taken as a yardstick (sections 33.3 and 33.12). In the particular case where only two groups are compared, multiple discriminant analysis reduces to Fisher’s linear discriminant function (chapter 32).

Suggested Citation

  • Pierre Jolicoeur, 1999. "Multiple discriminant analysis," Springer Books, in: Introduction to Biometry, chapter 0, pages 309-333, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-4777-8_34
    DOI: 10.1007/978-1-4615-4777-8_34
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