Title :
Sparse approximation based on wavelet kernel support vector machines
Author :
Yang, Dong-Kai ; Tong, Yu-Bing ; Zhang, Qi-Shan
Author_Institution :
Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China
Abstract :
For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel support vector machines, which can converge to minimum error with better sparsity. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.
Keywords :
approximation theory; convergence; source separation; sparse matrices; support vector machines; wavelet transforms; convergence; sparse approximation; support vector machines; wavelet approximation; wavelet kernel function; Approximation algorithms; Dictionaries; Discrete wavelet transforms; Electronic mail; Kernel; Matching pursuit algorithms; Packaging machines; Signal resolution; Support vector machines; Wavelet analysis; Sparse Approximation; Support Vector Machine; Wavelet Kernel Function;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527683
