Probabilistic model‐based discriminant analysis and clustering methods in chemometrics

Abstract

In Chemometrics, the supervised and unsupervised classification of high-dimensional data has become a recurrent problem. Model-based techniques for discriminant analysis and clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which up to now have been limited in their use within Chemometrics. The recent developments in model-based classification overcame these drawbacks and enabled the efficient classification of high-dimensional data. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection.