Linear discriminant analysis for two classes via recursive neural network reduction of the class separation

  • Mayer Aladjem
Learning Methodologies
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


A method for the linear discrimination of two classes is presented. It maximizes the Patrick-Fisher (PF) distance between the projected class-conditional densities. Since the PF distance is a highly nonlinear function, we propose a method, which searches for the directions corresponding to several large local maxima of the PF distance. Its novelty lies in a neural network transformation of the data along a found direction into data with deflated maxima of the PF distance and iteration to obtain the next direction. A simulation study indicates that the method has the potential to find the global maximum of the PF distance.


Neural networks for classification auto-associative network projection pursuit discriminant analysis statistical pattern recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Mayer Aladjem
    • 1
  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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