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KPCA basic thought is the first low-dimensional nonlinear relationships between the variables in the input space by non-linear Mapping in the high-dimensional feature space, and take place in a high-dimensional feature space principal components analysis, evaluating data in non- On the linear principal component projection. Specific forms of this nonlinear map does not need to seek, through appropriate inner product function implementation, which can be induced by the inner product function from the original input space implicit mapping to high dimensional feature space (nuclear Function), known as kernel principal component analysis. By selecting a different form of kernel functions, you can work with large amounts of nonlinear problems
cy2918789
2016-08-23
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