Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization

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 direction we maximize the objective function by an iterative optimization algorithm. The solution of this algorithm depends strongly on the starting 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 ensuring that a maximum already found will not be chosen again at a later stage. In this paper we re ne this method. We propose a procedure which provides a batch mode optimization instead an interactive optimization employed in 3 . By means...

• Type: Conference
• Year: 1999
• Where: IDA
• Authors: Mayer Aladjem
• Keywords: Class-conditional Densities, IDA 1999, Information Management, Iterative Optimization Algorithm, Objective Function