Chemometrics and intelligent laboratory systems vol:79 issue:1-2 pages:10-21
In this paper we first investigate the robustness of the SIMCA method for classifying high-dimensional observations. It turns out that both stages of the algorithm, the estimation of principal components and the construction of a classification rule, can be highly disturbed by the presence of outliers. Therefore we propose a robust procedure RSIMCA which is based on a robust Principal Component Analysis method for high-dimensional data (ROBPCA). Various simulations and real examples reveal the robustness of our approach. (c) 2005 Elsevier B.V. All rights reserved.