Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random
 

Summary: Nonparametric Linear Discriminant Analysis by
Recursive Optimization with Random
Initialization ?
Mayer Aladjem
Department of Electrical and Computer Engineering,
Ben-Gurion University of the Negev, P.O.B. 653,
84105 Beer-Sheva, Israel
aladjem@ee.bgu.ac.il
WWW home page: http: www.ee.bgu.ac.il faculty m a.html
Abstract. A method for the linear discrimination of two classes has
been proposed by us in 3 . It searches for the discriminant direction
which maximizes the 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 distance between the projected
densities is a highly nonlinear function with respect to the projected di-
rection we maximize the objective function by an iterative optimization
algorithm. The solution of this algorithm depends strongly on the start-
ing point of the optimizer and the observed maximum can be merely a
local maximum.In 3 we proposed a procedure for recursive optimization
which searches for several local maxima of the objective function ensur-

  

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

 

Collections: Computer Technologies and Information Sciences