Journal
AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS
Volume 43, Issue 2, Pages 147-177Publisher
BLACKWELL PUBL LTD
DOI: 10.1111/1467-842X.00164
Keywords
central subspaces; dimension reduction; regression; regression graphics; sliced inverse regression (SIR); sliced average variance estimation (SAVE)
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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.
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