A User's Guide to Principal Components [Hardcover]

J. Edward Jackson (Author)

Book Description

Publication Date: March 13, 1991 | ISBN-10: 0471622672 | ISBN-13: 978-0471622673 | Edition: 1

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

From the Reviews of A User’s Guide to Principal Components

"The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA."
–Technometrics

"I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results."
–Mathematical Geology

Editorial Reviews

From the Publisher

Principal component analysis is a multivariate technique in which a number of related variables are transformed to a (usually smaller) set of uncorrelated variables. This text is designed for practitioners of principal component analysis. Among the topics explored are extension to p variables, scaling input data, inferential procedures, operations with group data and vector interpretation. Dealing with the ``how-to-do-it'' as well as the ``why-it-works,'' it avoids getting bogged down in theoretical matters and computational techniques focusing instead on practical aspects of data reduction and interpretation.

From the Back Cover

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

From the Reviews of A User’s Guide to Principal Components

"The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA."
–Technometrics

"I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results."
–Mathematical Geology --This text refers to the Paperback edition.

Product Details

• Hardcover: 592 pages
• Publisher: Wiley-Interscience; 1 edition (March 13, 1991)
• Language: English
• ISBN-10: 0471622672
• ISBN-13: 978-0471622673
• Product Dimensions: 9.6 x 6.4 x 1.2 inches
• Shipping Weight: 2 pounds
• Average Customer Review: 3.6 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #2,870,495 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

16 of 24 people found the following review helpful:

5.0 out of 5 stars Principal Components Analysis, June 13, 2000

By 

Luis Martinez (New Orleans, Louisiana) - See all my reviews

This review is from: A User's Guide to Principal Components (Hardcover)

This book is an excellent choice for helping understand data compression and noise reduction of large datasets. It is extremely beneficial, especially when dealing with hyperspectral datasets, to understand the techniques involving the transformation of multiple bands into principal components. The book is well organized according to the general method(s) by which PCA works. From the compression of information content in a multiple number of bands, to other uses of principle components analysis, this is definitely an excellent reference for anyone who works with hyperspectral data.

4 of 6 people found the following review helpful:

5.0 out of 5 stars A guide for users, August 11, 2005

By 

R. Solimeno (Cincinnati, OH) - See all my reviews
(REAL NAME)   

This review is from: A User's Guide to Principal Components (Wiley Series in Probability and Statistics) (Paperback)

I find Jackson's book to be well-written and in a style that is almost conversational. He gives sound advice for stepwise evaluation of characteristic roots and residual analysis in Chapter 2. I have really only skimmed the surface with this book, but so far I like what I have read and am satisfied with the purchase.

2 of 4 people found the following review helpful:

3.0 out of 5 stars Symptom of a statistical approach I dislike, April 17, 2009

By 

Craig Garvin "Smart Grid Transitions" (Cambridge, MA USA) - See all my reviews
(REAL NAME)   

This review is from: A User's Guide to Principal Components (Wiley Series in Probability and Statistics) (Paperback)

I find Principal Component Analysis (PCA) a perfectly usable technique that has a place in a statistical toolbox. It is an unfortunate fact that in many applications areas, PCA has become the de-facto Multivatiate Analysis Technique, in some cases even becoming synonymous for that term. In an ideal world, a book like Jackson's would simply not be necessary. If more sophisticated analysis was required to solve a problem, any number of techniques far more powerful than PCA can be brought to bear. However, there is a user community that wants to augment PCA with multiple layers of secondary analysis and interpretation, and this book is for them.

Having stated my dislike for the need for this book, I concede that it meets that need quite well. It is written in an approachable manner, presents simple data sets, and is a little bit less math intensive than some of the more general machine learning texts.