Time-frequency learning machines

Abstract : 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.
Complete list of metadatas

Cited literature [29 references]  Display  Hide  Download

https://hal-ens-lyon.archives-ouvertes.fr/ensl-00118896
Contributor : Patrick Flandrin <>
Submitted on : Thursday, December 7, 2006 - 9:34:22 AM
Last modification on : Monday, September 16, 2019 - 4:35:44 PM
Long-term archiving on : Tuesday, April 6, 2010 - 11:59:21 PM

File

TFlearningmachines.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : ensl-00118896, version 1

Collections

Citation

Paul Honeiné, Cédric Richard, Patrick Flandrin. Time-frequency learning machines. 2006. ⟨ensl-00118896⟩

Share

Metrics

Record views

222

Files downloads

242