The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition.
A superior exposition of PCA, proof-oriented, efficiently, clear, and highly understandable. Contains all you would ever want to know on PCA. After years of hearing in academia PCA is when you take the eigenvectors of the covariance matrix, this book proves why you would even want to do that in the first place.