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Computational Statistics

, Volume 17, Issue 2, pp 251–271 | Cite as

Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons

  • António Pedro Duarte SilvaEmail author
Article

Summary

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.

Keywords

Principal Component Analysis Principal Variables Variable Selection All-Subsets Algorithms 

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Copyright information

© Physica-Verlag 2002

Authors and Affiliations

  1. 1.Faculdade de Economia e GestãoUniversidade Católica Portuguesa at PortoPortoPortugal

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