Sebastian Raschka • 9/14/2014

Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA

This technical article explains how to use the kernel trick and the Gaussian Radial Basis Function (RBF) kernel for nonlinear dimensionality reduction via Kernel Principal Component Analysis (kPCA). It covers the theory, provides a step-by-step implementation guide, and demonstrates its application on complex datasets like half-moons and concentric circles where standard linear PCA fails.

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#Machine Learning #dimensionality reduction #Kernel Pca