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Communication Dans Un Congrès Année : 2007

Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion

Résumé

In this paper, we propose a method for tuning time-frequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signal-dependent time-frequency analysis. The relevance of this approach of improving time-frequency classification accuracy is illustrated through examples.
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Dates et versions

hal-01966057 , version 1 (27-12-2018)

Identifiants

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Paul Honeine, Cédric Richard. Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion. Proc. IEEE workshop on Statistical Signal Processing (SSP), 2007, Madison, WI, USA, Unknown Region. pp.735 - 739, ⟨10.1109/SSP.2007.4301356⟩. ⟨hal-01966057⟩
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