Description: KPCA is a kernel-based principal components analysis, is a bridge between the linear to nonlinear. Nonlinear function to map the input space into a high dimensional space, in the middle of the feature space, data processing, the introduction of kernel function, product operation in the non-linear transformation of feature space for the kernel function of the original space calculation.
The basic idea is that the input space by some kind of hermit method is mapped to a higher dimensional space (feature space), and the PCA in the feature space. The algorithm is a detailed description of
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