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Response to the discussion of "Gaussian process regression for multivariate spectroscopic calibration"

Lookup NU author(s): Dr Tao Chen, Emeritus Professor Julian Morris, Professor Elaine Martin


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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.

Publication metadata

Author(s): Chen T, Morris J, Martin E

Publication type: Article

Publication status: Published

Journal: Chemometrics and Intelligent Laboratory Systems

Year: 2007

Volume: 87

Issue: 1

Pages: 95-97

ISSN (print): 0169-7439

ISSN (electronic): 1873-3239

Publisher: Elsevier BV


DOI: 10.1016/j.chemolab.2006.10.003


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