自动化学报2016，Vol.42Issue(4)：631-640,10.DOI:10.16383/j.aas.2016.c150296
基于压缩感知的多尺度最小二乘支持向量机
Multi-scale Least Squares Support Vector Machine Using Compressive Sensing
摘要
Abstract
A multi-scale least squares support vector machine (LS-SVM) based on compressive sensing (CS) and multi-resolution analysis (MRA) is proposed. First, a multi-scale LS-SVM model is conducted, in which a support vector kernel with the multi-resolution wavelet function is employed; then inspired by CS theory, sparse support vectors of multi-scale LS-SVM are constructed via least squares orthogonal matching pursuit (LS-OMP); finally, sparse support vectors are applied to function approximation. Simulation experiments demonstrate that the proposed method can estimate diverse details of signal by means of wavelet kernel with different scales. What is more, it can achieve good generalization performance with fewer support vectors, reducing the operation cost greatly, performing more superiorly compared to ordinary LS-SVM.关键词
最小二乘支持向量机/压缩感知/多尺度小波核/稀疏化/函数回归Key words
Least squares support vector machine (LS-SVM)/compressive sensing (CS)/multi-resolution wavelet kernel/sparse/function approximation引用本文复制引用
王琴,沈远彤..基于压缩感知的多尺度最小二乘支持向量机[J].自动化学报,2016,42(4):631-640,10.基金项目
国家自然科学基金(11301120)资助Supported by National Natural Science Foundation of China (11301120) （11301120）
