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Graphical Tools for Quadratic Discriminant Analysis

Summary: [This abstract is based on the authors’ abstract.] Dimension-reduction methods offer effective means to visualize discriminant analysis problems. It has been shown that the dimension-reduction method of sliced average variance estimation (SAVE) identifies variates that are equivalent to a quadratic discriminant analysis (QDA) solution. This study uses this connection to justify the use of SAVE variates in exploratory graphics for discriminant analysis. Classification can then be based on the SAVE variates using a suitable distance measure. If the measure selected is Mahalanobis distance, then classification is identical to QDA using the original variable. Given the lack of graphical tools for QDA in available software, this method is particularly useful and robust.

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  • Topics: Statistics
  • Keywords: Canonical analysis, Classification, Dimension reduction, Linear discriminant analysis, Quadratic discriminant analysis, Sliced average variance estimation, Dimensional analysis, Linar models, Estimation.
  • Author: Pardoe, Iain; Yin, Xiangrong; Cook, R. Dennis
  • Journal: Technometrics