Principal Components Analysis: Projection, Reconstruction, and Reduction
Chapter 12 formulates PCA as geometric optimization: maximize projected variance along a unit direction to obtain the leading eigenvector of the covariance matrix. The top k eigenvectors give both maximum retained variance and minimum linear reconstruction error.