Auto-correlation wavelet support vector machine

Authors:
G. Y. Chen;G. Dudek
Affiliations:
Center for Intelligence Machines, McGill University, McConnell Building, 3480 University Street, Montreal, Que., Canada H3A 2A7;Center for Intelligence Machines, McGill University, McConnell Building, 3480 University Street, Montreal, Que., Canada H3A 2A7
Venue:
Image and Vision Computing
Year:
2009

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Abstract

A support vector machine (SVM) with the auto-correlation of a compactly supported wavelet as a kernel is proposed in this paper. The authors prove that this kernel is an admissible support vector kernel. The main advantage of the auto-correlation of a compactly supported wavelet is that it satisfies the translation invariance property, which is very important for its use in signal processing. Also, we can choose a better wavelet by selecting from different wavelet families for our auto-correlation wavelet kernel. This is because for different applications we should choose wavelet filters selectively for the autocorrelation kernel. We should not always select the same wavelet filters independent of the application, as we demonstrate. Experiments on signal regression and pattern recognition show that this kernel is a feasible kernel for practical applications.