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From Feature Space to Primal Space: KPCA and Its Mixture Model
Wang Haixian * #
Research Center for Learning Science, Southeast University
*Correspondence author
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Funding: 教育部博士点基金,东南大学优秀青年教师教学科研资助计划（No.20070286030,）
Opened online:20 November 2009
Accepted by: none
Citation: Wang Haixian.From Feature Space to Primal Space: KPCA and Its Mixture Model[OL]. [20 November 2009] http://en.paper.edu.cn/en_releasepaper/content/36884
 
 
samples, we extend KPCA to a mixture of local KPCA models by applying the mixture model to probabilistic PCA in the primal space. The theoretical analysis and experimental results on both artificial and real data set have shown the superiority of the proposed methods in terms of computational efficiency and storage space, as well as recognition rate, especially when the number of data points $n$ is large.
Keywords:Kernel principal component analysis (KPCA);incomplete Cholesky decomposition;primal space;mixtures of kernel principal component analysis (MKPCA);expectation maximization (EM) algorithm;feature extraction
 
 
 

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