中文摘要：

分析了传统的主成分分析方法的不足,论述了KPCA方法及其时间复杂度高的缺陷。在此基础上,提出基于核函数构造的协方差矩阵的主成分分析,相比KPCA,该方法具有快的降维速度。实验结果显示：把该方法用于QAR数据具有良好的降维效果和高分类正确率。

英文摘要：

This paper analyzes the drawbacks of general Principal Component Analysis（PCA） firstly,and discusses the Kernel Principal Component Analysis（KPCA） and its drawbacks of high time complexity secondly.Then proposes the kernel function covariance matrix of principal component analysis in the end.Compared to KPCA,the method is fast descending dimension speed. The results show that the proposed method used for QAR data has a good effect of dimension reduction and high rate of correct classification.