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A User's Guide to Principal Components Paperback – September 10, 2003

ISBN-13: 978-0471471349 ISBN-10: 0471471348

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Product Details

  • Paperback: 592 pages
  • Publisher: Wiley-Interscience (September 10, 2003)
  • Language: English
  • ISBN-10: 0471471348
  • ISBN-13: 978-0471471349
  • Product Dimensions: 9.3 x 6.2 x 1 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 3.6 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #1,381,434 in Books (See Top 100 in Books)

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. --This text refers to an out of print or unavailable edition of this title.

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


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Customer Reviews

3.6 out of 5 stars
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Most Helpful Customer Reviews

16 of 24 people found the following review helpful By Luis Martinez on June 13, 2000
Format: 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.
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4 of 6 people found the following review helpful By R. Solimeno on August 11, 2005
Format: 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.
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3 of 8 people found the following review helpful By Craig Garvin on April 17, 2009
Format: 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.
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8 of 17 people found the following review helpful By Gadgester HALL OF FAME on February 12, 2003
Format: Hardcover
This book is geared toward engineering types, not for people who want to use PCA for stock trading, securities covariance forecasting, etc.
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0 of 17 people found the following review helpful By Kyoung Su Oh on June 26, 2007
Format: Paperback
I've received this book maybe 3 or 4weeks ago.
But I found that there is one problem.
The last several pages of that book are torn and folded.
I decided not to claim anything...
but I want for you to be more careful of your things(products).
Anyway, thank you for the delivery.
Sincerely yours.
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