Title
ROBPCA : a new approach to robust principal component analysis
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Washington, D.C. ,
Subject
Mathematics
Source (journal)
Technometrics : a journal of statistics for the physical, chemical, and engineering sciences. - Washington, D.C., 1959, currens
Volume/pages
47(2005) :1 , p. 64-79
ISSN
0040-1706
1537-2723
ISI
000226647400007
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
We introduce a new method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance matrix of the data and hence is highly sensitive to outlying observations. Two robust approaches have been developed to date. The first approach is based on the eigenvectors of a robust scatter matrix such as the minimum covariance determinant or an S-estimator and is limited to relatively low-dimensional data. The second approach is based on projection pursuit and can handle high-dimensional data. Here we propose the ROBPCA approach. which combines projection pursuit ideas with robust scatter matrix estimation. ROBPCA yields more accurate estimates at noncontaminated datasets and more robust estimates at contaminated data. ROBPCA can be computed rapidly. and is able to detect exact-fit situations. As a by-product. ROBPCA produces a diagnostic plot that displays and classifies the outliers. We apply the algorithm to several datasets from chemometrics and engineering.
E-info
https://repository.uantwerpen.be/docman/iruaauth/a56005/c895812.pdf
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