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Theory and Application Study of Feature Extraction Based on Kernel

XIAO Jianhua1,2,WU Jinpei2 (1.College of Mechanical science & Technology, Huazhong University of Science and Technology, Wuhan 430074; 2.Institute of Intelligent Technology & Systems, Wuyi University, Jiangmen 529020)  
This paper points out the drawbacks of the general PCA when it is used to solve nonlinear problem firstly. Then the kernel principal component analysis(KPCA) is introduced and used in the comprehensive evaluation. The research result shows that the KPCA has excellent performance of feature extraction and the KPCA with different kernel function can exhibit very similar accuracy. In the end of the paper, it indicate that active learning should be combined with KPCA for the purpose of improving the operation speed, reducing the demand of memory and decreasing the complexity.
【Fund】: 国家自然科学基金资助项目(7007023);; 广东省自然科学基金资助项目(021349)
【CateGory Index】: TP14
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