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Robust principal component analysis based on pairwise correlation estimators
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| Abstract |
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| 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. |
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| Language |
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English
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| Source (journal) |
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COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS |
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19th International Conference on Computational Statistics, (COMPSTAT'2010), AUG 22-27, 2010, Paris, FRANCE |
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| Publication |
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Heidelberg : Physica-verlag gmbh & co, 2010
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| ISBN |
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978-3-7908-2603-6
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(2010), p. 573-580
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| ISI |
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000395720500059
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| Full text (Publisher's DOI) |
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| Full text (publisher's version - intranet only) |
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