DISCARDING VARIABLES in PRINCIPAL COMPONENT ANALYSIS : ALGORITHMS for ALL-SUBSETS COMPARISONS
AbstractThe traditional approach to the interpretation of the results from a Principal Component Analysis implicitly discards variables that are weakly correlated with the most important and/or most interesting Principal Components. Some authors argue that this practice is potentially misleading and that it would be preferable to take a variable selection approach comparing variable subsets according to appropriate approximation criteria. In this paper, we propose algorithms for the comparison of all possible subsets according to some of the most important criteria proposed to date. The computational effort of the proposed algorithms is studied and it is shown that, given current computer technology, they are feasible for problems involving up to 30 variables. A software implementation is freely available on the internet.
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Bibliographic InfoPaper provided by Faculdade de Economia e Gestão, Universidade Católica Portuguesa (Porto) in its series Working Papers de Economia (Economics Working Papers) with number 02.
Length: 42 pages
Date of creation: Jan 2000
Date of revision:
Principal Component Analysis; Principal Variables; Variable Selection; All-Subsets Algorithms.;
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