为了提高图像分类的识别率，在对基于核的学习算法中，核函数的构成条件以及不同核函数的特性进行分析和研究的基础上，提出了一种新的核函数——组合核函数，并将它应用于核主分量分析(KPCA)中，以便进行图像特征的提取，由于新的核函数既可以提取全局特征，又可以提取局部特征，因此，可以提高KPCA在图像特征提取中的性能。为了验证所提出核函数的有效性，首先利用新的核函数进行KPCA，以便对手写数字和脸谱等图像进行特征提取，然后利用线性支持向量机(SVM)来进行识别，实验结果显示，从识别率上看，用组合核函数所提取的特征质量比原核函数所提取的特征质量高。

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.