Cui, C;
Fearn, T;
(2017)
Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration.
Journal of Near Infrared Spectroscopy
, 25
(1)
pp. 5-14.
10.1177/0967033516678515.
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Comparison of PLSR, LS-SVMs and GPR for a Near Infrared Calibration.pdf - Accepted Version Download (1MB) | Preview |
Abstract
This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional linear regression model, partial least squares regression on an agricultural example. The non linear models, least squares support vector machines, and Gaussian process regression, showed enhanced generalization ability, especially in maintaining homogeneous prediction accuracy over the range. The two non-linear models generally have similar prediction performance, but showed different features in some situations, especially when the size of the training set varies. This is due to fundamental differences in fitting criteria between these models.
Type: | Article |
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Title: | Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1177/0967033516678515 |
Publisher version: | https://doi.org/10.1177/0967033516678515 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Near-Infrared spectroscopy; Multivariate calibration; Partial least squares regression; Least squares support vector machines; Gaussian process regression |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1555754 |
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