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. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Van Aelst, S., Vandervieren, E., & Willems, G. (2010). Robust principal component analysis based on pairwise correlation estimators. In Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers (pp. 573–580). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-7908-2604-3_59
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