Robust SIMCA-bounding influence of outliers

被引：38

作者：

Department of Chemometrics, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland ^{[1
]}

不详 ^{[2
]}

机构：

[1] Department of Chemometrics, The University of Silesia, 40-006 Katowice

[2] Department of Analytical Chemistry and Pharmaceutical Technology, Vrije Universiteit Brussel, B-1090 Brussels

来源：

Chemometr. Intelligent Lab. Syst. | 2007年 / 1卷 / 95-103期

关键词：

Leverages; Outliers; Robust classification; Robust PCA;

D O I：

10.1016/j.chemolab.2006.10.003

中图分类号：

学科分类号：

摘要：

In this article a robust version of SIMCA, based on spherical principal component analysis [N. Locantore, J.S. Marron, D.G. Simpson, N. Tripoli, J.T. Zhang, K.L. Cohen, Robust principal component analysis for functional data (with comments), Test 8 (1999) 1-74], is introduced for chemometrics community. The efficiency of the new approach is compared to the classical SIMCA and to its robust version proposed by Vanden Branden et al. [K. Vanden Branden, M. Hubert, Robust classification in high dimensions based on the SIMCA method, Chemometrics and Intelligent Laboratory Systems 79 (2005) 10-21]. The performances of the presented approaches are evaluated on simulated and real data sets. The results obtained from a simulation study give evidence that the proposed robust SIMCA approach offers a satisfactory efficiency when the model set does not contain outliers and is also robust, what ensures a proper classification of new objects even, when the model set used to derive classification rules is contaminated to a large extent by outlying objects. © 2006 Elsevier B.V. All rights reserved.

引用

页码：95 / 103

页数：8

共 17 条

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