Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components

A common objective in exploratory multivariate analysis is to identify a subset of the variables which conveys the main features of the whole sample. Analysis of a well-known multivariate data set shows that methods currently available for selecting variables in principal component analysis may not...

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Bibliographic Details
Published in:Applied Statistics 1987-01, Vol.36 (1), p.22-33
Main Author: Krzanowski, W. J.
Format: Article
Language:English
Subjects:
Applied statistics
Approximation
Data models
Datasets
Dimensionality
Discriminant analysis
Exact sciences and technology
Group structure
Mathematics
Modeling
Monte Carlo methods
Monte Carlo simulation
Multivariate analysis
Principal component analysis
Principal components analysis
Probability and statistics
Procrustes rotation
Sciences and techniques of general use
Singular value decomposition
Statistics
Variable selection
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Summary:A common objective in exploratory multivariate analysis is to identify a subset of the variables which conveys the main features of the whole sample. Analysis of a well-known multivariate data set shows that methods currently available for selecting variables in principal component analysis may not lead to an appropriate subset. A new selection method, based on Procrustes Analysis, is proposed and shown to lead to a better subset for the data first analysed. Some supporting Monte Carlo results are presented, and implications for other multivariate techniques are briefly discussed.
ISSN:0035-9254
1467-9876
DOI:10.2307/2347842