| ROBUST PRINCIPAL COMPONENT ANALYSIS BY PROJECTION PURSUIT |
| |
| 作者姓名： | YU-LONG XIE JI-HONG WANG YI-ZENG LIANG LI-XIAN SUN XIN-HUA SONG RU-QIN YU |
| |
| 作者单位： | YU-LONG XIE JI-HONG WANG YI-ZENG LIANG LI-XIAN SUN XIN-HUA SONG RU-QIN YU Department of Chemistry and Chemical Engineering，Hunan University，Changsha 410082，People's Republic of China |
| |
| 摘 要： | 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.
|
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.ROBUST PRINCIPAL COMPONENT ANALYSIS BY PROJECTION PURSUIT[J].Journal of Geographical Sciences,1993(6). |
| |
| Authors: | YU-LONG XIE JI-HONG WANG YI-ZENG LIANG LI-XIAN SUN XIN-HUA SONG RU-QIN YU |
| |
| Institution: | YU-LONG XIE JI-HONG WANG YI-ZENG LIANG LI-XIAN SUN XIN-HUA SONG RU-QIN YU Department of Chemistry and Chemical Engineering,Hunan University,Changsh,People's Republic of China |
| |
| Abstract: | 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. |
| |
| Keywords: | Principal component analysis Projection pursuit Simulated annealing algorithm Robust statistics |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《地理学报(英文版)》浏览原始摘要信息 |
| 点击此处可从《地理学报(英文版)》下载免费的PDF全文 |
