自动化学报2017，Vol.43Issue(1)：132-141,10.DOI:10.16383/j.aas.2017.c150720
一种基于全局代表点的快速最小二乘支持向量机稀疏化算法
A Fast Sparse Algorithm for Least Squares Support Vector Machine Based on Global Representative Points
摘要
Abstract
For lack of sparseness on least squares support vector machine (LS-SVM), the study on sparsity of LS-SVM is an important topic. Currently, most of the sparse LS-SVM methods are based on the iteration selection strategy. Consequently, they do not perform well in computation complexity and sparsity. To improve the performance of sparse LS-SVM method, a fast method, global-representation-based sparse least squares support vector machine (GRS-LSSVM), is proposed based on the selection of global representative points in this paper. To evaluate datum0s representation, an index is given based on local density and global discrete degree. In the algorithm, firstly, the top global representative data are selected from all data in one step using the index to construct the support vector set of sparse LS-SVM, and then the set is used to compute the decision hyperplane of sparse LS-SVM. This algorithm explores the non-iteration on sparse LS-SVM. Experimental results show that the proposed method has higher sparseness degree, more stability, and lower computational complexity than the traditional iteration algorithms.关键词
最小二乘支持向量机/稀疏化/全局代表点/局部密度/全局离散度Key words
Least squares support vector machine (LS-SVM)/sparseness/global representative point/local density/global discrete degree引用本文复制引用
马跃峰,梁循,周小平..一种基于全局代表点的快速最小二乘支持向量机稀疏化算法[J].自动化学报,2017,43(1):132-141,10.基金项目
国家自然科学基金(71531012,71271211),北京市自然科学基金(4132067),中国人民大学品牌计划(10XNI029),京东商城电子商务研究项目(413313012)资助Supported by National Natural Science Foundation of China (71531012,71271211), Natural Science Foundation of Beijing (4132067), Brand Project of Renmin University (10XNI029), E-commerce Research Project of Jingdong Mall (413313012) （71531012,71271211）
