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Two-Class Pattern Discrimination via Recursive Optimization of Patrick-Fisher Distance
 

Summary: Two-Class Pattern Discrimination via Recursive Optimization
of Patrick-Fisher Distance
Mayer E. 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 searches for the discriminant direction
which maximizes the Patrick-Fisher (PF) distance
between the projected class-conditional densities. It is a
nonparametric method, in the sense that the densities are
estimated from the data. Since the PF distance is a highly
nonlinear function, we propose a recursive optimization
procedure for searching the directions corresponding to
several large local maxima of the PF distance. Its novelty
lies in the transformation of the data along a found
direction into data with deflated maxima of PF distance
and iteration to obtain the next direction. A simulation

  

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

 

Collections: Computer Technologies and Information Sciences