Cui, C;
Fearn, T;
(2018)
Hierarchical mixture of linear regressions for multivariate spectroscopic calibration: An application for NIR calibration.
Chemometrics and Intelligent Laboratory Systems
, 174
pp. 1-14.
10.1016/j.chemolab.2017.12.013.
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Abstract
This paper investigates the use of the hierarchical mixture of linear regressions (HMLR) and variational inference for multivariate spectroscopic calibration. The performance of HMLR is compared to the classical methods: partial least squares regression (PLSR), and PLS embedded locally weighted regression (LWR) on three different NIR datasets, including a publicly accessible one. In these tests, HMLR outperformed the other two benchmark methods. Compared to LWR, HMLR is parametric, which makes it interpretable and easy to use. In addition, HMLR provides a novel calibration scheme to build a two-tier PLS regression model automatically. This is especially useful when the investigated constituent covers a large range.
| Type: | Article |
|---|---|
| Title: | Hierarchical mixture of linear regressions for multivariate spectroscopic calibration: An application for NIR calibration |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.chemolab.2017.12.013 |
| Publisher version: | https://doi.org/10.1016/j.chemolab.2017.12.013 |
| 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, Hierarchical mixture of linear regressions, Expectation maximization, Variational inference |
| 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/10042756 |
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