Principal Component Analysis

by Ian T. Jolliffe

Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines.
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.
Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra.
Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.

Genres: Mathematics, Science

518 pages, Hardcover

First published January 1, 1986

Reviews

[Joseph Bronski · Rating 5 out of 5]

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.