A Parameter Selection Optimization Algorithm for Kernel Principal Components Regression

摘要：

Kernel principal component analysis can extract the nonlinear features of the data, but the performance is great impact by the parameter of kernel function. This paper presents a kernel parameters optimization method which based on a piecewise binary encoding. The experimental results are very good by using the approach to optimize the kernel parameters in the case of unknown the distribution of the data, which indicating the effectiveness of our method.

关键词： kernel principal components analysis feature spaces optimization algorithm binary encoding

作者: CHEN Jianghong

作者单位: College of Science, China Three Gorges University, Yichang, P.R.China, 443002

会议类型: 国际会议

会议名称: The 3rd International Institute of Statistics & Management Engineering Symposium(2010 国际统计与管理工程研讨会 IISMES)

会议地点: 威海

会议语种:英文

页码: 459-463

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）