DETERMINE THE PARAMETER OF KERNEL DISCRIMINANT ANALYSIS IN ACCORDANCE WITH FISHER CRITERION

摘要：

Feature extraction performance of kernel discriminant analysis (KDA) is influenced by the value of the parameter of the kernel function.Usually one is hard to effectively exert the performance of FDA for it is not easy to determine the optimal value for the kernel parameter.Though some approaches have been proposed to automatically determine the parameter of FDA, it seems that none of these approaches takes the nature of FDA into account in selecting the value for the kernel parameter.In this paper, we develop a novel parameter selection approach that is subject to the essence of Fisher discriminant analysis.This approach is theoretically able to achieve the kernel parameter that is associated with a feature space with satisfactory linear separability.The approach can be carried out using an iterative computation procedure.Experimental results show that the developed approach does result in much higher classification accuracy than naive KDA.

关键词： Kernel discriminant analysis (KDA) Kernel function Parameter selection Fisher criterion

作者: YONG XU WEI-JIE LI

作者单位: Department of Computer Science & Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518005, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2931-2935

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）