MANIFOLD BASED KERNEL OPTIMIZATION FOR KPCA

摘要：

This paper presents a manifold based kernel optimizing algorithm for KPCA which has recently shown effectiveness for pattern recognition and systematic classification based on extracting nonlinear features. However, their performances largely depend on the kernel function. Current methods simply choose the kernel function empirically or experimentally from a given set of candidates. We use manifold learning to improve the kernel function, which is capable to discover the nonlinear degrees of freedom that underlie complex natural observations. In contrast to previous algorithms for kernel optimization, ours efficiently computes a globally optimal solution that is guaranteed to converge asymptotically to the true structure and extracts the nonlinear features better. Experiments show that the method performed well in the field of pattern recognition.

关键词： KPCA manifold learning kernel optimization heuristic algorithm supervised classification

作者: Li Zeng Bin Chen Linping Du Kejia Xu

作者单位: Department of Machine Vision Institute of Chengdu Computer Application Chinese Academy of Science Chengdu, China

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 69-72

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）