Premium
Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
Author(s) -
Cook R. Dennis,
Yin Xiangrong
Publication year - 2001
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/1467-842x.00164
Subject(s) - sliced inverse regression , mathematics , dimensionality reduction , linear discriminant analysis , sufficient dimension reduction , visualization , dimension (graph theory) , discriminant , statistics , regression , pattern recognition (psychology) , artificial intelligence , regression analysis , computer science , combinatorics
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.