Support Vector Machine Based Multiresolution Signal Approximation

Multiresolution signal approximation (MSA) provides a simple hierarchical approximation of the signals. And support vector machine (SVM) has been introduced as a novel tool for solving approximation problems. Based on the fact that scale subspaces onto which MSA projects the signals are reproducing kernel Hilbert spaces (RKHS), we integrate the approximation criterion of SVM into MSA and then an SVM based MSA (S-MSA) algorithm is proposed. Experiments exhibit that S-MSA owns better approximation accuracy and smoothness than MSA. Furthermore, quantitative comparison with MSA illustrates the robustness of S-MSA when noises are present