Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
Read OriginalThis 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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