ISSN:
0886-9383
Keywords:
Principal component analysis
;
Projection pursuit
;
Simulated annealing algorithm
;
Robust statistics
;
Chemistry
;
Analytical Chemistry and Spectroscopy
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
Notes:
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.
Additional Material:
7 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/cem.1180070606
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