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Robust principal component analysis by projection pursuit
Author(s) -
Xie YuLong,
Wang JiHong,
Liang YiZeng,
Sun LiXian,
Song XinHua,
Yu RuQin
Publication year - 1993
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180070606
Subject(s) - principal component analysis , projection pursuit , outlier , chemometrics , projection (relational algebra) , computer science , pattern recognition (psychology) , robust principal component analysis , mathematics , simulated annealing , component analysis , variance (accounting) , sparse pca , artificial intelligence , algorithm , mathematical optimization , machine learning , accounting , business
Principal component analysis (PCA) is a widely used technique in chemometrics. The classical PCA method is, unfortunately, non‐robust, since the variance is adopted as the objective function. In this paper, projection pursuit (PP) is used to carry out PCA with a criterion which is more robust than the variance. In addition, the generalized simulated 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 method is especially recommended for use in cases with possible outlier(s) existing in the data.