Dimension reduction and visualization in discriminant analysis (with discussion)

R. Dennis Cook, Xiangrong Yin

Research output: Contribution to journalArticlepeer-review

148 Scopus citations


This paper discusses visualization methods for discriminant analysis. It does not address numerical methods for classification per se, but rather focuses on graphical methods that can be viewed as pre-processors, aiding the analyst's understanding of the data and the choice of a final classifier. The methods are adaptations of recent results in dimension reduction for regression, including sliced inverse regression and sliced average variance estimation. A permutation test is suggested as a means of determining dimension, and examples are given throughout the discussion.

Original languageEnglish (US)
Pages (from-to)147-199
Number of pages53
JournalAustralian and New Zealand Journal of Statistics
Issue number2
StatePublished - Jun 2001

Bibliographical note

Copyright 2018 Elsevier B.V., All rights reserved.


  • Central subspaces
  • Dimension reduction
  • Regression
  • Regression graphics
  • Sliced average variance estimation (SAVE)
  • Sliced inverse regression (SIR)


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