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Nonstationary Signal Analysis with Kernel Machines
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Author(s): Paul Honeine (Institut Charles Delaunay, France), Cédric Richard (Institut Charles Delaunay, France)and Patrick Flandrin (Ecole Normale Supérieure de Lyon, France)
Copyright: 2010
Pages: 19
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch010
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Abstract
This chapter introduces machine learning for nonstationary signal analysis and classification. It argues that machine learning based on the theory of reproducing kernels can be extended to nonstationary signal analysis and classification. The authors show that some specific reproducing kernels allow pattern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the selection of the reproducing kernel for a nonstationary signal classification problem. For this purpose, the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency representation for a given classification problem. These links offer new perspectives in the field of nonstationary signal analysis, which can benefit from recent developments of statistical learning theory and pattern recognition.
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