 
Summary: Nonparametric Linear Discriminant Analysis by
Recursive Optimization with Random
Initialization ?
Mayer Aladjem
Department of Electrical and Computer Engineering,
BenGurion University of the Negev, P.O.B. 653,
84105 BeerSheva, 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 classconditional
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
