A0502
Title: Optimizing interval PLS via GP regression
Authors: Nicolas Hernandez - Queen Mary University of London (United Kingdom) [presenting]
Tom Fearn - UCL (United Kingdom)
Yoonsun Choi - UCL (United Kingdom)
Abstract: Interval partial least-squares regression (iPLS) is an adaptation of the partial least squares regression (PLS) tailored for high dimensional spectral data, such as near-infrared spectra. Spectrometric data is expressed over a continuous domain. Therefore, interval selection is a more viable alternative for feature extraction than variable selection. Despite its potential, a primary challenge in iPLS remains in the selection of optimal intervals. Although traditional approaches, such as forward and backward selection methods, have practical benefits, they have crucial limitations of heavy reliance on heuristic approaches. The aim is to propose a novel approach to interval selection in iPLS via uncertainty quantification techniques. Gaussian process regression is used, emphasizing its ability for flexible modelling and its provision of uncertainty estimates. This integration aims to optimize the accuracy of interval selection to highlight discrepancies between model predictions and observations. The contribution is in evolving dialogue on improving spectral data analysis techniques in the iPLS domain, with an application to the Spectrometric field.
