One norm linear programming support vector regression

作     者：Tanveer, Mohammad Mangal, Mohit Ahmad, Izhar Shao, Yuan-Hai 

作者机构：Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore LNM Inst Informat Technol Dept Commun & Comp Engn Jaipur 302031 Rajasthan India King Fahd Univ Petr & Minerals Dept Math & Stat Dhahran 31261 Saudi Arabia Zhejiang Univ Technol Zhijiang Coll Hangzhou 310024 Zhejiang Peoples R China 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2016年第173卷第Part3期

页      面：1508-1518页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Linear programming 1-Norm support vector machines Newton method Unconstrained convex minimization 

摘      要：In this paper, a new linear programming formulation of a 1-norm support vector regression (SVR) is proposed whose solution is obtained by solving an exterior penalty problem in the dual space as an unconstrained minimization problem using Newton method. The solution of modified unconstrained minimization problem reduces to solving just system of linear equations as opposed to solving quadratic programming problem in SVR, which leads to extremely simple and fast algorithm. The algorithm converges from any starting point and can be easily implemented in MATLAB without using any optimization packages. The main advantage of the proposed approach is that it leads to a robust and sparse model representation meaning that many components of the optimal solution vector will become zero and therefore the decision function can be determined using much less number of support vectors in comparison to SVR, smooth SVR (SSVR) and weighted SVR (WSVR). To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world benchmark datasets. Similar or better generalization performance of the proposed method in less training time in comparison with SVR, SSVR and WSVR clearly exhibits its suitability and applicability. (C) 2015 Elsevier B.V. All rights reserved.