Principal Component Analysis

by Ian T. Jolliffe

Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Examples * Graphical Representation of Data Using Principal Components * Choosing a Subset of Principal Components or Variables * Principal Component Analysis and Factor Analysis * Principal Components in Regression Analysis * Principal Components Used with Other Multivariate Techniques * Outlier Detection, Influential Observations and Robust Estimation * Rotation and Interpretation of Principal Components * Principal Component Analysis for Time Series and Other Non-Independent Data * Principal Component Analysis for Special Types of Data * Generalizations and Adaptations of Principal Component Analysis

Genres: Mathematics, Science

271 pages, Hardcover

First published January 1, 1986

Reviews

[Joseph Bronski]

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.