Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
Read OriginalThis technical article introduces kernel methods for nonlinear dimensionality reduction, focusing on Gaussian Radial Basis Function (RBF) Kernel PCA. It contrasts linear PCA with kernel PCA, explains the kernel trick, and provides a step-by-step implementation guide. The content includes examples like half-moon shapes and the Swiss roll to demonstrate the technique's effectiveness on linearly inseparable data.
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