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.
https://hal.archives-ouvertes.fr/hal-00459947
Contributeur : Julien Jacques
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Soumis le : jeudi 25 février 2010 - 15:42:10
Dernière modification le : mercredi 25 avril 2018 - 14:26:06
Document(s) archivé(s) le : vendredi 18 juin 2010 - 21:55:20
Julien Jacques, Charles Bouveyron, Stéphane Girard, Olivier Devos, Ludovic Duponchel, et al.. Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data. 2010. 〈hal-00459947v1〉
