%0 Journal Article
%T Improving Performance of Kernel Principal Component Analysis Using Combination Kernel Functions<br>利用组合核函数提高核主分量分析的性能
%A KONG Rui
%A SHI Ze-sheng
%A GUO Li
%A ZHANG Guo-xuan
%A <br>孔锐
%A 施泽生
%A 郭立
%A 张国宣
%J 中国图象图形学报
%D 2004
%I 
%X In the paper, the formation conditions and the characteristics of kernel functions are researched and analysed which are used in kernel principal component analysis algorithm. Kernel principal component analysis algorithm is a new statistic signal processing technique which can extract nonlinear features of images. Kernel functions are key elements for improving it's performance. A new kernel function-combination kernel function is proposed. The new kernel function combines a local kernel function with a global kernel function. The local kernel is conditionally positive definite kernel which can extract local features of images. The global kernel function is polynomial kernel function which can extract global features of images. So the new kernel function can extract not only local features but also global features of images. The new kernel function is applied in kernel principal component analysis for extracting features of images. The test images are MNIST handwriting digits and ORL face database. Features of images are extracting by kernel principal component analysis firstly. Then performing classification by using linear support vector machines, the effect of the new kernel and that of other kernel on kernel principal component analysis are compared. The experiment results indicate the new kernel function certainly improves the performance of kernel principal component analysis.
%K Kernel principal component analysis
%K Kernel functions
%K Principal component analysis
%K Support vector machines<br>核主分量分析
%K 核函数
%K 主分量分析
%K 支持向量机
%K 统计模式识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=F2D0C5468895AEE0&yid=D0E58B75BFD8E51C&vid=9CF7A0430CBB2DFD&iid=CA4FD0336C81A37A&sid=1371F55DA51B6E64&eid=94E7F66E6C42FA23&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=13&reference_num=9