Time-Frequency Learning Machines

Paul Honeine (1) , Cédric Richard (1) , Patrick Flandrin (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 Phys-ENS - Laboratoire de Physique de l'ENS Lyon

Over the last decade, the theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces. Our paper is a further contribution which extends the framework of the so-called kernel learning machines to time-frequency analysis, showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.

Mots clés: Time-frequency analysis; kernel machines; learning theory; support vector machines

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]; Statistiques [math.ST]; Traitement du signal et de l'image [eess.SP]; Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Ordinateur et société [cs.CY]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Paul Honeine, Cédric Richard, Patrick Flandrin. Time-Frequency Learning Machines. IEEE Transactions on Signal Processing, 2007, 55 (7), pp.3930-3936. ⟨10.1109/TSP.2007.894252⟩. ⟨hal-02111309⟩