Sparse and Robust Least Squares Support Vector Machine: a Linear Programming Formulation

摘要：

Least squares support vector machine (LS-SVM) has an outstanding advantage of lower computational complexity than that of standard support vector machines. Its shortcomings are the loss of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization performance. In this paper, a least squares support vector machine with linear programming formulation (LS-SVM-LP) is proposed to deal with above shortcomings. This method is equivalent to solve a linear equation set with deficient rank just like the over complete problem in independent component analysis (ICA). A minimum of 1-norm based object function is chosen to get the sparse and robust solution based on the idea of basis pursuit (BP) in the whole feasible region. Some UCI datasets are used to demonstrate the effectiveness of this model. The experimental results show that LS-SVM-LP can obtain a small number of support vectors and improve the generalization ability of LS-SVM.

作者: Liwei Wei Zhenyu Chen Jianping Li Weixuan Xu

作者单位: Chinese Academy of Sciences ,Beijing, China

会议类型: 国际会议

会议名称: 2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)

会议地点: 南京

会议语种:英文

在线出版日期: 2007-11-18（万方平台首次上网日期，不代表论文的发表时间）