Dimensionality Reduction

Dimensionality reduction projects high-dimensional data into lower-dimensional spaces for visualization, compression, or denoising. Methods include linear techniques (PCA, LDA), manifold methods (t-SNE, UMAP), and learned approaches using autoencoders.