High-dimensional data is hard to visualize, slow to process, and prone to overfitting. Dimensionality reduction compresses features into a smaller set while preserving as much useful structure as possible.

Linear methods like principal component analysis project data onto directions of maximum variance. Nonlinear methods like t-SNE and UMAP preserve local neighbourhoods, which makes them popular for visualizing clusters.

Common techniques

• Principal component analysis
• Linear discriminant analysis
• t-SNE for visualization
• UMAP for manifold learning
• Autoencoders for nonlinear compression

Reduction always loses something. The goal is to drop noise and redundancy while keeping signal. Choosing the right number of dimensions is a judgment call, often guided by downstream performance rather than reconstruction error alone.