Wavelet kernel Support Vector Machines for sparse approximation |
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Authors: | Yubing Tong Dongkai Yang Qishan Zhang |
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Affiliation: | Dept of Electronic Information Engineering, Beijing University of Aeronautics and astronautics, Beijing 100083, China |
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Abstract: | Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines. |
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Keywords: | Wavelet kernel function Support Vector Machines (SVM) Sparse approximation Quadratic Programming (QP) |
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