A union of incoherent spaces model for classification

Abstract : We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low misclassification, the subspaces should be incoherent so that features of a given class cannot represent efficiently signals from another. We propose a simple iterative strategy to learn dictionaries which are are the same time good for approximating within a class and also discriminant. Preliminary tests on a standard face images database show competitive results.
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Communication dans un congrès
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, Mar 2010, Dallas, United States. pp.5490 -5493, 2010, 〈10.1109/ICASSP.2010.5495208〉
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https://hal.inria.fr/inria-00568895
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : mercredi 23 février 2011 - 18:01:23
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

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Karin Schnass, Pierre Vandergheynst. A union of incoherent spaces model for classification. Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, Mar 2010, Dallas, United States. pp.5490 -5493, 2010, 〈10.1109/ICASSP.2010.5495208〉. 〈inria-00568895〉

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