Publication
Title
ROBPCA : a new approach to robust principal component analysis
Author
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.
Language
English
Source (journal)
Technometrics : a journal of statistics for the physical, chemical, and engineering sciences. - Washington, D.C., 1959, currens
Publication
Washington, D.C. : 2005
ISSN
0040-1706 [print]
1537-2723 [online]
Volume/pages
47:1(2005), p. 64-79
ISI
000226647400007
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 20.09.2017