Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data

Abstract : In this work, a family of generative Gaussian models designed for the supervised classification of high-dimensional data is presented as well as the associated classification method called High Dimensional Discriminant Analysis (HDDA). The advantages of these Gaussian models are: i) the representation of the input density model is smooth; ii) the data of each class are modeled in a specific subspace of low dimensionality; iii) each class may have its own covariance structure; iv) regularization is coupled to the classification criterion to avoid data over-fitting. To illustrate the abilities of the method, HDDA is applied on complex high-dimensional multi-class classification problems in mid-infrared and near infrared spectroscopy and compared to state-of-the-art methods.
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Journal of Chemometrics, Wiley, 2010, 24 (11-12), pp.719-727. 〈10.1002/cem.1355〉
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Julien Jacques, Charles Bouveyron, Stephane Girard, Olivier Devos, Ludovic Duponchel, et al.. Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data. Journal of Chemometrics, Wiley, 2010, 24 (11-12), pp.719-727. 〈10.1002/cem.1355〉. 〈hal-00459947v2〉

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