Principal Component Analysis | 
 enlarge | Author: I.t. Jolliffe
Publisher: Springer
Category: Book
List Price: $115.00
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Rating:  1 reviews
Sales Rank: 137808
Media: Hardcover
Edition: 2nd
Pages: 502
Number Of Items: 1
Shipping Weight (lbs): 1.7
Dimensions (in): 9.1 x 6.2 x 1.2
ISBN: 0387954422
Dewey Decimal Number: 519.535
EAN: 9780387954424
Publication Date: October 1, 2002
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Product Description
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.
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| Customer Reviews:
Most authoritative book on PCA-related techniques December 17, 2002
DataGuru (DC)
14 out of 17 found this review helpful
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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