关键词: 快速存取记录器（QAR）数据, 主成分分析, 核矩阵, 相似性

Abstract: This paper analyzes the problems of traditional Principal Component Analysis（PCA） when comparing the similarity of QAR data. The Kernel Principal Component Analysis（KPCA） based on EROS is proposed to deal with these problems. This paper introduces EROS method without vector treatment and adopts the kernel matrix of principal component analysis to reduce the dimension of QAR data. This paper gives classification on two groups of QAR data sets by using support vector products method with selecting different number of principal component, and compares it with SPCA and GPCA method. The results show that the proposed method used for QAR data has a good effect on classification.

Key words: Quick Access Recorder（QAR） data, Principal Component Analysis, kernel matrix, similarity