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

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  • B. Baris Alkan
  • Cemal Atakan
  • Nesrin Alkan

Abstract

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.

Suggested Citation

  • B. Baris Alkan & Cemal Atakan & Nesrin Alkan, 2015. "A comparison of different procedures for principal component analysis in the presence of outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1716-1722, August.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1716-1722
    DOI: 10.1080/02664763.2015.1005063
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    1. N. A. Campbell, 1980. "Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(3), pages 231-237, November.
    2. Sunil Sapra, 2010. "Robust vs. classical principalcomponent analysis in the presence of outliers," Applied Economics Letters, Taylor & Francis Journals, vol. 17(6), pages 519-523.
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    1. B. Barış Alkan, 2016. "Robust Principal Component Analysis Based on Modified Minimum Covariance Determinant in the Presence of Outliers," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 85-94, September.

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