Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
✍ Scribed by Julien Jacques; Charles Bouveyron; Stéphane Girard; Olivier Devos; Ludovic Duponchel; Cyril Ruckebusch
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 631 KB
- Volume
- 24
- Category
- Article
- ISSN
- 0886-9383
- DOI
- 10.1002/cem.1355
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✦ Synopsis
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.