A comparison of different procedures for principal component analysis in the presence of outliers

Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation-maxim...

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Bibliographic Details
Published in:Journal of applied statistics 2015-08, Vol.42 (8), p.1716-1722
Main Authors: Alkan, B. Bariş, Atakan, Cemal, Alkan, Nesrin
Format: Article
Language:English
Subjects:
Applied statistics
Covariance
Data analysis
Data collection
Determinants
Estimates
Estimating techniques
expectation-maximization
missing value
multiple imputation
outliers
Outliers (statistics)
Principal component analysis
Principal components analysis
Reduction
Statistical methods
Studies
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Summary:Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation-maximization - EM and multiple imputation - MI instead of outliers were examined on the artificial and a real data set. Furthermore, robust PCA based on minimum covariance determinant (MCD), PCA based on estimates obtained by EM instead of outliers and PCA based on estimates obtained by MI instead of outliers were compared with the results of CPCA. In this study, we tried to show the effects of using estimates obtained by MI and EM instead of outliers, depending on the ratio of outliers in data set. Finally, when the ratio of outliers exceeds 20%, we suggest the use of estimates obtained by MI and EM instead of outliers as an alternative approach.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2015.1005063