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Semi-supervised learning in multivariate calibration

E. V. Thomas

Published 2019 in Chemometrics and Intelligent Laboratory Systems

ABSTRACT

Abstract Multivariate calibration has historically been associated with supervised learning where a model developed/learned from a set of labeled data is used to predict characteristics of an object by using measurements obtained from it. For example, in analytical chemistry, the characteristics are often related to composition, some form of spectroscopy is commonly used to acquire measurements, and the object is typically a sample of some material of interest. Recently in this and other contexts there has been an increased interest in semi-supervised learning where a combination of both labeled and unlabeled data are used to help produce a predictive model. The use of semi-supervised learning can be advantageous in certain situations in multivariate calibration involving both forward and inverse modeling approaches. Potential advantages of models developed via semi-supervised learning are illustrated for the two modeling approaches via a series of simulations that are derived from near-infrared reflectance spectra. The basis and conditions for benefits of a semi-supervised learning strategy for multivariate calibration are identified and discussed. In the case of both modeling approaches, the primary advantage of semi-supervised learning was found to be a reduction in conditional prediction bias. This advantage is most likely to be realized when the quantity of labeled data is small, the quantity of unlabeled data is large, and when the values of the characteristics to be predicted are distant from the centroid of associated values of the labeled data.

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