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Regularized Kernel Discriminant Analysis with Optimally Scaled Data

In: Measurement and Multivariate Analysis

Author

Listed:
  • Halima Bensmail

    (326/336 Stokely Management Ctr., Department of Statistics)

  • Hamparsum Bozdogan

    (326/336 Stokely Management Ctr., Department of Statistics)

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.

Suggested Citation

  • Halima Bensmail & Hamparsum Bozdogan, 2002. "Regularized Kernel Discriminant Analysis with Optimally Scaled Data," Springer Books, in: Shizuhiko Nishisato & Yasumasa Baba & Hamparsum Bozdogan & Koji Kanefuji (ed.), Measurement and Multivariate Analysis, pages 133-144, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-65955-6_14
    DOI: 10.1007/978-4-431-65955-6_14
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