Robust vs. classical principalcomponent analysis in the presence of outliers

Abstract

Principal Component Analysis (PCA) is a very versatile technique for dimension reduction in multivariate data. Classical PCA is very sensitive to outliers and can lead to misleading conclusions in the presence of outliers. This article studies the merits of robust PCA relative to classical PCA when outliers are present. An algorithm due to Filzmoser et al. (2006) based on a modification of the projection pursuit algorithm of Croux and Ruiz-Gazen (2005) is used for robust PCA computations for a financial data set as well as simulated data sets. Our simulation results indicate that robust PCA generally leads to greater reduction in model dimension than classical PCA in data sets with outliers.