Discarding variables in a principal component analysis: algorithms for all-subsets comparisons

António Pedro Duarte Silva*

*Autor correspondente para este trabalho

Resultado de pesquisarevisão de pares

21 Citações (Scopus)

Resumo

The 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 is 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 comparison 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 thirty variables. A free-domain software implementation can be downloaded from the Internet.
Idioma originalEnglish
Páginas (de-até)251-271
Número de páginas21
RevistaComputational Statistics
Volume17
Número de emissão2
DOIs
Estado da publicaçãoPublicado - 2002

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