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(More customer reviews)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 (SSA0, 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 (Developments in Atmospheric Sciences).
The area of SSA has been developing fast and several
monographs have appeared already, e.g. Analysis of Time Series Structure: SSA and Related Techniques. The area of indpedent component analysis is another one that has attracted increasing
attention in recent years, Independent Component Analysis. 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!
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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.
Buy cheap Principal Component Analysis (Springer Series in Statistics) now.
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