Development of Kernel Fisher Discriminant Model Using the Cross-Entropy Method

In this paper, the cross-entropy (CE) method is proposed to solve non-linear discriminant analysis or kernel Fisher discriminant (CE-KFD) analysis. CE through certain steps can find the optimal or near optimal solution with a fast rate of convergence for optimization problem. While, KFD is to solve problem of Fisher´s linear discriminant in a kernel feature space F by maximizing between class variance and minimizing within class variance. Through the numerical experiments, we found that CE-KFD demonstrates the high accuracy of the results compared to the traditional methods, Fisher LDA and kernel Fisher (KFD) with eigen decomposition method.