Optimal selection of time-frequency representations for signal classification: A kernel-target alignment approach
Résumé
In this paper, we propose a method for selecting time-frequency distributions appropriate for given learning tasks. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignment. This criterion makes possible to find the optimal representation for a given classification problem without designing the classifier itself. Some possible applications of our framework are discussed. The first one provides a computationally attractive way of adjusting the free parameters of a distribution to improve classification performance. The second one is related to the selection, from a set of candidates, of the distribution that best facilitates a classification task. The last one addresses the problem of optimally combining several distributions
Mots clés
Kernel machines
Classification
Optimal representation
non-stationarity
learning (artificial intelligence)
signal classification
signal representation
time-frequency analysis
time-frequency representations
kernel-target alignment approach
time-frequency distributions
Time frequency analysis
Pattern classification
Kernel
Hilbert space
Machine learning
Distributed computing
Support vector machines
Support vector machine classification
Appropriate technology
Signal analysis
| Origine | Fichiers produits par l'(les) auteur(s) |
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