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

António Pedro Duarte Silva ()
Additional contact information
António Pedro Duarte Silva: Universidade Católica Portuguesa at Porto

Computational Statistics, 2002, vol. 17, issue 2, No 7, 271 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s001800200105 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200105

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s001800200105

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().