Robust Principal Component Analysis Based on Pairwise Correlation Estimators

Stefan Van Aelst (), Ellen Vandervieren () and Gert Willems ()
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Stefan Van Aelst: Ghent University, Dept. of Applied Mathematics and Computer Science
Ellen Vandervieren: University of Antwerp, Dept. of Mathematics and Computer Science
Gert Willems: Ghent University, Dept. of Applied Mathematics and Computer Science

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 573-580 from Springer

Abstract: Abstract Principal component analysis tries to explain and simplify the structure of multivariate data. For standardized variables, these principal components correspond to the eigenvectors of their correlation matrix. To obtain a robust principal components analysis, we estimate this correlation matrix componentwise by using robust pairwise correlation estimates. We show that the approach based on pairwise correlation estimators does not need a majority of outlier-free observations which becomes very useful for high dimensional problems. We further demonstrate that the “bivariate trimming” method especially works well in this setting.

Keywords: principal component analysis; robustness; high dimensional data; trimming (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_59

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DOI: 10.1007/978-3-7908-2604-3_59

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