- Series: Springer Series in Statistics
- Hardcover: 488 pages
- Publisher: Springer; 2nd edition (October 1, 2002)
- Language: English
- ISBN-10: 0387954422
- ISBN-13: 978-0387954424
- Product Dimensions: 6.1 x 1.1 x 9.2 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 5 customer reviews
- Amazon Best Sellers Rank: #1,983,276 in Books (See Top 100 in Books)
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Principal Component Analysis (Springer Series in Statistics) 2nd Edition
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From the reviews of the second edition:
"Bringing the 1E up to date has added more than 200 pages of additional text. Anyone seriously involved with the application of PCA will certainly want to purchase a copy…Seldom has such a wealth of material on a single topic in statistics appeared in one book…All that material has gotten a whole lot more comprehensive here in this new edition. Goodall (1988) also labeled the book ‘a good read.’ Now it may be a little heavy for that purpose, but it certainly is a fantastic reference book."
ISI SHORT BOOK REVIEWS
"This is the bible of principal component analysis (PCA). This second edition of the book is nearly twice the length of the first. [Short Book Reviews, Vol.6, p.45] New material includes discussion of ordination methods linked to PCA, including biplots, determining the number of components to retain, extended discussion of outlier detection, stability, and sensitivity, simplifying PCAs to aid interpretation, time series data, size/shape data, and nonlinear PCA, including the Gifi system and neural networks, and other topics. As can be seen from this, the book is not a narrow discussion of PCA, but links it effectively and in an illuminating way to a wide variety of other multivariate statistical tools.
Principal component analysis is the empirical manifestation of the eigen value-decomposition of a correlation or covariance matrix. The fact that a book of nearly 500 pages can be written on this, and noting the author's comment that 'it is certain that I have missed some topics, and my coverage of others will be too brief for the taste of some readers' drives home the extent to whch statistics exceeds mere mathematics.
This book is an invaluable reference work and I am pleased to have it on my shelves. My only regret is that I probably will not have time to read it from cover to cover with the attention it deserves."
JOURNAL OF CLASSIFICATION
"This revised edition presents much new information on methods developed since the 1986 edition. Some of these newer parts include the expanded discussion of ordination and scaling methods (e.g., biplots), selection of the number of components to retain, canonical correlation for comparing groups of variables, independent correlation analysis for non-normal data, and principal curves."
"This book is one of the very few texts entirely devoted to principal component analysis (PCA). The second edition is usefully expanded and updated from the first edition; thus it is very well worth considering this edition, even if one is familiar with the first. … Very nice features are the carefully discussed links between PCA and related techniques … . Throughout, numerous references to relevant literature are provided. This book will be useful as an introduction to PCA as well as a reference." (Marieke E. Timmerman, Journal of the American Statistical Association, 2004)
"This is another volume in the Springer Series in Statistics … which has consistently produced books of high quality and generally advanced treatment of the topic. This revised edition presents much new information on methods developed since the 1986 edition, and these are well described … ." (William Shannon, Journal of Classification, Vol. 21 (1), 2004)
"The first edition of this book (IE), published in 1986, was the first book devoted entirely to principal component analysis (PCA). … Bringing the IE up to date has added more than 200 pages of additional text. Anyone seriously involved with the application of PCA will certainly want to purchase a copy. … Seldom has such a wealth of material on a single topic in statistics appeared in one book. … it certainly is a fantastic reference book." (Technometrics, Vol. 45 (3), 2003)
"This is the Bible of principle components analysis (PCA). This second edition of the book is nearly twice the length of the first. … The book is an invaluable reference work and I am pleased to have it on my shelves." (D. J. Hand, Short Book Reviews, Issue 2, 2003)
Top customer reviews
The math level required for this books is merely being familiar with linear algebra and any matrix operations as well as basic statistics concepts, and should be suitable to any senior level or graduate level students, or application researchers in various fields. In contrast to those full of mathematical formula and symbols, this book is a pleasure to read yet it doesn't sacrifice mathematical rigor.
I like the broadness and depth of this book. It covers almost all major aspects of PCA related topics and a wide range of real applications. The theories and applications are made clear enough for anyone to understand the materials yet it doesn't go very deep, so that readers need to explore the cited works for further indepth study. I personally like this style because it is a good balance of theory and application and it actually serves as a road map for anyone who have further interests in specific topics.
In the future editions, I hope the author can put even more weight on developments of recent decade, such as its application in data mining.
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!