Gaussian Mass Optimization for Kernel PCA Parameters
This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesnt possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MMST handwriting database, the recognition rate improves by 1.6％ compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.
PCA kernel parameter optimization Guassian mass object recognition handwriting recognition
Liu Yong Wang ZuLin
School of Electronic Information Engineering Beihang University Beijing, China
国际会议
2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)
长沙
英文
84-87
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）