Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 58-63 |
| Number of pages | 6 |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| Volume | 139 |
| DOIs | |
| State | Published - Dec 15 2014 |
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Keywords
- Class modeling
- Fault diagnosis
- MATLAB toolbox
- Nonlinear and robust algorithms
- One-class partial least squares (OCPLS) classifiers
ASJC Scopus subject areas
- Analytical Chemistry
- Software
- Computer Science Applications
- Process Chemistry and Technology
- Spectroscopy
Cite this
A MATLAB toolbox for class modeling using one-class partial least squares (OCPLS) classifiers. / Xu, Lu; Goodarzi, Mohammad; Shi, Wei; Cai, Chen Bo; Jiang, Jian Hui.
In: Chemometrics and Intelligent Laboratory Systems, Vol. 139, 15.12.2014, p. 58-63.Research output: Contribution to journal › Article
}
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
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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UR - http://www.scopus.com/inward/citedby.url?scp=84957059868&partnerID=8YFLogxK
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 -