Dimensionality Reduction

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Reducing the number of features while preserving important information. Techniques include PCA (linear), t-SNE (visualization), UMAP (fast nonlinear), and autoencoders (neural). Combats the curse of dimensionality.

ml unsupervised-learning dimensionality-reduction