ROBUST PRINCIPAL COMPONENT ANALYSIS BY PROJECTION PURSUIT     

YU-LONG XIE JI-HONG WANG YI-ZENG LIANG LI-XIAN SUN XIN-HUA SONG RU-QIN YU Department of Chemistry  Chemical Engineering  Hunan University  Changsh  People''s Republic of China 《地理学报(英文版)》1993,(6)

Principal component analysis (PCA) is a widely used technique in chemometrics.The classical PCAmethod is,unfortunately,non-robust,since the variance is adopted as the objective function.In thispaper,projection pursuit (PP) is used to carry out PCA with a criterion which is more robust than thevariance.In addition,the generalized simulated annealing (GSA) algorithm is introduced as anoptimization procedure in the process of PP calculation to guarantee the global optimum.The resultsfor simulated data sets show that PCA via PP is resistant to the deviation of the error distribution fromthe normal one.The method is especially recommended for use in cases with possible outlier(s) existingin the data.  相似文献