Principal component analysis

A dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated linear combinations called principal components, ordered by the variance they explain. Based on eigenvalue decomposition of the covariance or correlation matrix. Widely used for data compression, visualization, and multicollinearity reduction.