Principal Component Analysis

Rating:
Summary: Most authoritative book on PCA-related techniques
Review: The first edition of this book was the most authoritative book on this subject 15 years ago. Now the author has provided us with a much-needed second edition since there are many developments since. Principal components related techniques are the main dimension-reduction methods in analysis of multivariate data. Since there is much redundancy with high throughput measurements such as spatial, spectra, or image data, thus the need to compress or decompose data. Related but somewhat different techniques include SVD, singular spectrum analysis (SSA), PC regression, shrinkage, EoF, etc.
Main consumers of PCA-related methods include chemometrics, climate analysis, and image analysis A very nice book in the
area of climate analysis is Principal Component Analysis in Meteorology and Oceanography by Rudolph W. Preisendorfer (1988).
The area of SSA has been developing fast and several
monographs have appeared already. The area of indpedent component analysis is another one that has attracted increasing
attention in recent years. Most of these recent developments have been covered and more. With this, I strongly recommend this book for readers who use multivariate analysis extensively and who need to keep abreast of the fast growing PCA tools which are as important as ever!