Title :
Signal-Dependent Time-Frequency Representations for Classification using a Radially Gaussian Kernel and the Alignment Criterion
Author :
Honeine, Paul ; Richard, Cédric
Author_Institution :
Institut Charles Delaunay (FRE CNRS 2848) - LM2S - Université de Technologie de Troyes, 12 rue Marie Curie, BP 2060, 10010 Troyes cedex, France - fax. +33.3.25.71.56.99, paul.honeine@utt.fr (tel. +33.3.25.71.56.25)
Abstract :
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.
Keywords :
Computational efficiency; Interference; Kernel; Machine learning; Pattern recognition; Signal analysis; Signal design; Support vector machine classification; Support vector machines; Time frequency analysis;
Conference_Titel :
Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on
Conference_Location :
Madison, WI, USA
Print_ISBN :
978-1-4244-1198-6
Electronic_ISBN :
978-1-4244-1198-6
DOI :
10.1109/SSP.2007.4301356