Component-wise dimension reduction

Principal components methods and factor analysis are popular tools for the dimension-reduction problem. These techniques can be used to obtain a smaller number of new variables. However, the new variables may include all or most of the original variables. In this study, two methods are given which w...

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Bibliographic Details
Published inJournal of statistical planning and inference Vol. 114; no. 1; pp. 81 - 93
Main Authors Fedorov, Valerii V., Herzberg, Agnes M., Leonov, Sergei L.
Format Journal Article
LanguageEnglish
Published Lausanne Elsevier B.V 01.06.2003
New York,NY Elsevier Science
Amsterdam
Subjects
Dimension reduction
Exact sciences and technology
Experimental design
Mathematics
Multivariate analysis
Optimal design
Principal components
Probability and statistics
Sciences and techniques of general use
Statistics
Dimension reduction
62K05
Principal components
Optimal design
Linear prediction
Reduction
Factor analysis
Error estimation
Integral equation
Dimension
Covariance matrix
Multivariate analysis
Principal component analysis
Method study
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Summary:Principal components methods and factor analysis are popular tools for the dimension-reduction problem. These techniques can be used to obtain a smaller number of new variables. However, the new variables may include all or most of the original variables. In this study, two methods are given which will select the most informative subset of variables from the variables which are directly measured. The different approaches are compared in a concluding example.
ISSN:0378-3758
1873-1171
DOI:10.1016/S0378-3758(02)00464-0