We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Variable selection and interpretation in correlation principal components.
- Authors
Noriah M. Al‐Kandari; Ian T. Jolliffe
- Abstract
Principal component analysis (PCA) is a dimension‐reducing tool that replaces the variables in a multivariate data set by a smaller number of derived variables. Dimension reduction is often undertaken to help in interpreting the data set but, as each principal component usually involves all the original variables, interpretation of a PCA can still be difficult. One way to overcome this difficulty is to select a subset of the original variables and use this subset to approximate the principal components. This article reviews a number of techniques for choosing subsets of the variables and examines their merits in terms of preserving the information in the PCA, and in aiding interpretation of the main sources of variation in the data. Copyright © 2005 John Wiley & Sons, Ltd.
- Subjects
MULTIVARIATE analysis; STATISTICAL correlation; MATHEMATICAL statistics; REGRESSION analysis
- Publication
Environmetrics, 2005, Vol 16, Issue 6, p659
- ISSN
1180-4009
- Publication type
Academic Journal
- DOI
10.1002/env.728