Regularized Kernel Discriminant Analysis with Optimally Scaled Data

Halima Bensmail and Hamparsum Bozdogan
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Halima Bensmail: 326/336 Stokely Management Ctr., Department of Statistics
Hamparsum Bozdogan: 326/336 Stokely Management Ctr., Department of Statistics

A chapter in Measurement and Multivariate Analysis, 2002, pp 133-144 from Springer

Abstract: Summary Linear discriminant analysis is a well known procedure for discrimination where the linear predictors define one set of variables and a set of dummy variables representing class membership which defines the other set. Here we propose a new method of discriminating between observations using a set of mixed (i.e., categorical and/or continuous) variables. This nonparametric discriminant procedure optimally scales the data and estimates the distribution of the object scores using multivariate kernel density estimation. We propose using Bozdogan’s information-theoretic measure complexity ICOMP to select both the window width of the kernel density estimator as well as the dimension of the object scores matrix.

Keywords: Linear Discriminant Analysis; Window Width; Kernel Density Estimator; Canonical Discriminant Analysis; Object Score (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-65955-6_14

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DOI: 10.1007/978-4-431-65955-6_14

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