14 min read
Why Does PCA Work? A Geometric and Statistical Derivation
A step-by-step derivation of principal component analysis, connecting variance maximization, covariance eigenvectors, and optimal linear reconstruction.
Blog topic
Linear Algebra
2 articles on this topic.
12 min read
Eigenvectors: From Linear Transformations to Principal Component Analysis
A mathematical journey from eigenvectors and covariance matrices to PCA, including the maximum-variance derivation, geometric projections, explained variance, and practical limitations.