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

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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 features of these Gaussian models are as follows: 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) model 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. Copyright © 2010 John Wiley & Sons, Ltd.

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