%0 Journal Article %1 Daszykowski:simca %A Daszykowski, M. %A Kaczmarek, K. %A Stanimirova, I. %A Heyden, Y. Vander %A Walczak, B. %D 2007 %J Chemometrics and Intelligent Laboratory Systems %K classification outliers pca robust simca %N 1 %P 95 - 103 %R DOI: 10.1016/j.chemolab.2006.10.003 %T Robust SIMCA-bounding influence of outliers %U http://www.sciencedirect.com/science/article/B6TFP-4MFJT2P-1/2/92be74f8e42c46d16adbcbe2da5e21de %V 87 %X 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.

@article{Daszykowski:simca, abstract = {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.}, added-at = {2010-03-02T11:08:28.000+0100}, author = {Daszykowski, M. and Kaczmarek, K. and Stanimirova, I. and Heyden, Y. Vander and Walczak, B.}, biburl = {https://www.bibsonomy.org/bibtex/2e8c590f5524e563e36e3425a3a26b148/vivion}, description = {ScienceDirect - Chemometrics and Intelligent Laboratory Systems : Robust SIMCA-bounding influence of outliers}, doi = {DOI: 10.1016/j.chemolab.2006.10.003}, interhash = {b07f24c1272abd8d574239abdedaf379}, intrahash = {e8c590f5524e563e36e3425a3a26b148}, issn = {0169-7439}, journal = {Chemometrics and Intelligent Laboratory Systems}, keywords = {classification outliers pca robust simca}, note = {Selected papers presented at the Conferentia Chemometrica 2005 Hajdúszoboszló, Hungary 28-31 August 2005 - Conferentia Chemometrica 2005}, number = 1, pages = {95 - 103}, timestamp = {2010-06-03T10:17:21.000+0200}, title = {Robust SIMCA-bounding influence of outliers}, url = {http://www.sciencedirect.com/science/article/B6TFP-4MFJT2P-1/2/92be74f8e42c46d16adbcbe2da5e21de}, volume = 87, year = 2007 }