Probabilistic Analysis of Kernel Principal Components
نویسندگان
چکیده
This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (ii) an effective classification scheme. It turns out that the original loading matrix is replaced by a newly defined empirical loading matrix. The expectation/maximization algorithm for learning parameters of interest is also presented.
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