KPCA
于 2013-04-30 发布
文件大小:479KB
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代码说明：
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can eciently compute principal components in high{ dimensional feature spaces, related to input space by some nonlinear map for instance the space of all possible d{pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition
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