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Gaussian mixture models for the classification of high‐dimensional vibrational spectroscopy data
Author(s) -
Jacques Julien,
Bouveyron Charles,
Girard Stéphane,
Devos Olivier,
Duponchel Ludovic,
Ruckebusch Cyril
Publication year - 2010
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1355
Subject(s) - subspace topology , covariance , pattern recognition (psychology) , gaussian , chemometrics , artificial intelligence , curse of dimensionality , mixture model , clustering high dimensional data , linear discriminant analysis , computer science , regularization (linguistics) , representation (politics) , class (philosophy) , algorithm , mathematics , machine learning , chemistry , statistics , cluster analysis , computational chemistry , politics , political science , law
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.