Kernel Principal Component Analysis

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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