摘要
Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.
Principal Component Analysis （PCA） is one of the most important feature extraction methods, and Kernel Principal Component Analysis （KPCA） is a nonlinear extension of PCA based on kernel methods. In real world, each input data may not be fully assigned to one class and it may partially belong to other classes. Based on the theory of fuzzy sets, this paper presents Fuzzy Principal Component Analysis （FPCA） and its nonlinear extension model, i.e., Kernel-based Fuzzy Principal Component Analysis （KFPCA）. The experimental results indicate that the proposed algorithms have good performances.
关键词
计算机技术
网络设计
设计方案
通信技术
信息处理
Principal Component Analysis （PCA）
Kernel methods
Fuzzy PCA （FPCA）
Kernel PCA （KPCA）
