TY - JOUR
T1 - A MATLAB toolbox for class modeling using one-class partial least squares (OCPLS) classifiers
AU - Xu, Lu
AU - Goodarzi, Mohammad
AU - Shi, Wei
AU - Cai, Chen Bo
AU - Jiang, Jian Hui
N1 - Funding Information:
This work was financially supported by Guizhou Provincial Department of Education 125 Plan Major Project No. QJH2013(027). The authors are in great debt to Dr. S. Serneels and Dr. C. Croux for their generosity to allow us to adjust their MATLAB code for PRM and include it in this toolbox to perform robust OCPLS. We are also grateful to Dr. Paolo Oliveri, Dr. M. Isabel López, Dr. Adriano A. Gomes, Dr. Licarion Pinto and Dr. Mário Cesar Ugulino de Araújo, as well as other reviewers for testing the MATLAB codes and reviewing the manuscript.
Publisher Copyright:
© 2014 Elsevier B.V.
PY - 2014/12/15
Y1 - 2014/12/15
N2 - One-class classifiers are widely used to solve the classification problems where control or class modeling of a target class is necessary, e.g., untargeted analysis of food adulterations and frauds, tracing the origins of a food with Protected Denomination of Origin, fault diagnosis, etc. Recently, one-class partial least squares (OCPLS) has been developed and demonstrated to be a useful technique for class modeling. For analysis of nonlinear and outlier-contaminated data, nonlinear and robust OCPLS algorithms are required. This paper describes a free MATLAB toolbox for class modeling using OCPLS classifiers. The toolbox includes ordinary, nonlinear and robust OCPLS methods. The nonlinear algorithm is based on the Gaussian radial basis function (GRBF), and the robust algorithm is based on the partial robust M-regression (PRM). The usage of the toolbox is demonstrated by analysis of a real data set.
AB - One-class classifiers are widely used to solve the classification problems where control or class modeling of a target class is necessary, e.g., untargeted analysis of food adulterations and frauds, tracing the origins of a food with Protected Denomination of Origin, fault diagnosis, etc. Recently, one-class partial least squares (OCPLS) has been developed and demonstrated to be a useful technique for class modeling. For analysis of nonlinear and outlier-contaminated data, nonlinear and robust OCPLS algorithms are required. This paper describes a free MATLAB toolbox for class modeling using OCPLS classifiers. The toolbox includes ordinary, nonlinear and robust OCPLS methods. The nonlinear algorithm is based on the Gaussian radial basis function (GRBF), and the robust algorithm is based on the partial robust M-regression (PRM). The usage of the toolbox is demonstrated by analysis of a real data set.
KW - Class modeling
KW - Fault diagnosis
KW - MATLAB toolbox
KW - Nonlinear and robust algorithms
KW - One-class partial least squares (OCPLS) classifiers
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U2 - 10.1016/j.chemolab.2014.09.005
DO - 10.1016/j.chemolab.2014.09.005
M3 - Article
AN - SCOPUS:84957059868
VL - 139
SP - 58
EP - 63
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
ER -