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25* -Conference 2nd Int. Workshop on Statistical Techniques in Pattern Recognition,
 

Summary: 25* - Conference
2nd Int. Workshop on Statistical Techniques in Pattern Recognition,
Sydney, Australia, August 11-13, 1998 (in press)
1
Linear Discriminant Analysis for Two Classes via
Recursive Neural Network Reduction of the Class
Separation*
Mayer Aladjem
Department of Electrical and Computer Engineering
Ben-Gurion University of the Negev, P.O.B. 653, 84105 Beer-Sheva,
Israel, e-mail: aladjem@bguee.bgu.ac.il
Abstract. 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.
Keywords: Neural networks for classification, auto-associative network, projection pursuit,
discriminant analysis, statistical pattern recognition.

  

Source: Aladjem, Mayer - Department of Electrical and Computer Engineering, Ben-Gurion University

 

Collections: Computer Technologies and Information Sciences