Publication
Title
Principal component analysis for data containing outliers and missing elements
Author
Abstract
Two approaches are presented to perform principal component analysis (PCA) on data which contain both outlying cases and missing elements. At first an eigendecomposition of a covariance matrix which can deal with such data is proposed, but this approach is not fit for data where the number of variables exceeds the number of cases. Alternatively, an expectation robust (ER) algorithm is proposed so as to adapt the existing methodology for robust PCA to data containing missing elements. According to an extensive simulation study, the ER approach performs well for all data sizes concerned. Using simulations and an example, it is shown that by virtue of the ER algorithm, the properties of the existing methods for robust PCA carry through to data with missing elements. (C) 2007 Elsevier B.V. All rights reserved.
Language
English
Source (journal)
Computational statistics and data analysis. - Amsterdam
Publication
Amsterdam : 2008
ISSN
0167-9473
Volume/pages
52:3(2008), p. 1712-1727
ISI
000253669700033
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 29.02.2012
Last edited 23.06.2017
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