Kernel Principal Component Analysis
Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.
Read more about Kernel Principal Component Analysis: Linear PCA, Introduction of The Kernel To PCA, Large Datasets, Example, Applications
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