A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking  

作　　者：HU Lei YI Guoxing HUANG Chao 

机构地区：[1]School of Astronautics,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第1期151-162,共12页系统工程与电子技术（英文版）

基　　金：supported by the Science and Technology on Space Intelligent Control Laboratory for National Defense(KGJZDSYS-2018-08)。

摘　　要：Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.

关 键 词：least square support vector regression(LSSVR) global representative point ranking(GRPR) initial training dataset pruning strategy sparsity regression accuracy 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]