Discarding variables in a principal component analysis: algorithms for all-subsets comparisons
type: Item
instance of: scholarly article
title: Discarding variables in a principal component analysis: algorithms for all-subsets comparisons
zbMATH Open document ID: 1010.62052
author: António Pedro Duarte Silva
published in: Computational Statistics
publication date: 2003-02-06T00:00:00Z
review text: In multivariate data analysis, when the number of original variables is large, the principal components themselves may be difficult to interpret and, in order to surpass this problem, the variables that have low correlations with those principal components are usually ignored. But this practice is potentially misleading and it may be preferable to search for small subsets of the original variables that approximate the relevant principal components in some optimal way. The author proposes algorithms for the comparison of all possible subsets according to some most important comparison criteria proposed by McCabe; Robert and Escoufier; Cadima and Jolliffe. The branch and bound algorithms based on adaptations of Furnival and Wilson's implicit enumeration algorithm for regression analysis are presented. The computational effort of the proposed algorithms is studied.
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 1865451
zbMATH Keywords: discarding variables
zbMATH Keywords: principal components analysis
zbMATH Keywords: algorithms for all-subsets comparisons
reviewed by: Aleksandr D. Borisenko
MaRDI profile type: Publication
full work available at URL: s001800200105
OpenAlex ID: W1504930956
DOI: 10.1007/S001800200105
description: scientific article; zbMATH DE number 1865451