ROBUST PRINCIPAL COMPONENT ANALYSIS BY PROJECTION PURSUIT

Principal component analysis (PCA) is a widely used technique in chemo metrics. The classical PCA method is, unfortunately, non-robust, since the variance is adopted as the objective function. In this paper, pro jection pursuit (PP) is used to carry out PCA with a criterion which i s more robust than the variance. In addition, the generalized simulate d annealing (GSA) algorithm is introduced as an optimization procedure in the process of PP calculation to guarantee the global optimum. The results for simulated data sets show that PCA via PP is resistant to the deviation of the error distribution from the normal one. The metho d is especially recommended for use in cases with possible outlier(s) existing in the data.