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Dimension reduction and visualization in discriminant analysis. With a discussion. (English) Zbl 0992.62056
Summary: 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.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62-09 Graphical methods in statistics (MSC2010)
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