Target Classification by a New Class of Linear Discriminants

  • Arezki Halet
  • George A. Lampropoulos
  • Tûe Huynh

Abstract

A new linear discriminant technique that results in better classification performance over existing techniques is presented in this paper. This new approach is formulated in a similar manner to that of the Fisher linear discriminant. However, the matrix which corresponds to within classes has been replaced by a new matrix. This matrix takes into consideration the cross-correlation properties of the classes of interest. It has been shown through simulations that this matrix replacement results in a better classification performance over other linear discrimination methods including the Fisher discriminant. Finally, the proposed new discriminant is presented in parametric and nonparametric forms, and is found to exhibit better classification in both cases over other parametric and nonparametric methods. With this new approach, the nonparametric method will prove to be more successful than its parametric counterpart. The feature selection is also discussed.

Keywords

Feature Selection Scatter Matrix Good Classification Performance Minimum Classification Error Fisher Linear Discriminant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Arezki Halet
    • 1
  • George A. Lampropoulos
    • 1
  • Tûe Huynh
    • 2
  1. 1.A.U.G. Signals Ltd.TorontoCanada
  2. 2.Département de Génie ÉlectriqueUniversité LavalCanada

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