Principal Component Analysis

Karl Pearson, 1901

Pearson introduced principal components as directions that successively maximize variance after orthogonal projection. This exhibit fits a toy correlated cloud by estimating the 2×2 covariance matrix’s leading eigenvector—gold line through the mean—then animates points sliding onto their PC₁ coordinates along rose guide segments.