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Communication Dans Un Congrès Année : 2006

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
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Dates et versions

hal-01966060 , version 1 (27-12-2018)

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Paul Honeine, Cédric Richard, Patrick Flandrin, Jean-Baptiste Pothin. Optimal selection of time-frequency representations for signal classification: A kernel-target alignment approach. Proc. 31st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006, Toulouse, France. ⟨10.1109/ICASSP.2006.1660694⟩. ⟨hal-01966060⟩
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