Dimensionality Reduction

Dimensionality reduction transforms data with many features into a smaller representation. Techniques such as principal component analysis, autoencoders, t-SNE, and UMAP serve different goals, including compression, visualization, noise reduction, faster training, and mitigation of the curse of dimensionality. A reduced space can reveal clusters or simplify downstream modeling, but it always discards or reshapes some information. Visual maps can be especially misleading when local and global distances are distorted. Practitioners should select a method that matches the task, fit it only on appropriate training data, document the retained variance or reconstruction quality, and verify that important minority patterns were not erased.