Title :
A novel support vector machine kernel based on Slepian semi-wavelets
Author_Institution :
Dept. of Math., Ohio Univ., Athens, OH, USA
Abstract :
In this paper, we construct a positive definite kernel associated with Slepian semi-wavelets. The kernel possesses multiscale structure and exhibits a strong localization property. It is convolution type associated with asymptotic sparse Gram matrix and allows the use of thresholding methods. We then focus on developing practical numerical algorithm to compute the kernel. Applications of the kernel in the context of kernel adaptive filtering are discussed.
Keywords :
Hilbert spaces; adaptive filters; convolution; sparse matrices; support vector machines; wavelet transforms; Slepian semiwavelets-based support vector machine kernel; asymptotic sparse Gram matrix; kernel adaptive filtering; localization property; multiscale structure; numerical algorithm; positive definite kernel; thresholding methods; Hilbert space; Kernel; Machine learning; Presses; Uncertainty; Wave functions; Wavelet transforms;
Conference_Titel :
Aerospace and Electronics Conference (NAECON), Proceedings of the 2011 IEEE National
Conference_Location :
Dayton, OH
Print_ISBN :
978-1-4577-1040-7
DOI :
10.1109/NAECON.2011.6183079
