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Chemometrics and Intelligent Laboratory Systems, Vol. 79, No. 1-2. (October 2005), pp. 10-21, doi:10.1016/j.chemolab.2005.03.002 Key: citeulike:12176491
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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.
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