Smooth twin support vector regression

Authors:
Xiaobo Chen;Jian Yang;Jun Liang;Qiaolin Ye
Affiliations:
Nanjing University of Science and Technology, School of Computer Science and Technology, 210094, Nanjing, People’s Republic of China and Jiangsu University, School of Computer Science and T ...;Nanjing University of Science and Technology, School of Computer Science and Technology, 210094, Nanjing, People’s Republic of China;Jiangsu University, School of Computer Science and Telecommunication Engineering, 212013, Zhenjiang, People’s Republic of China;Nanjing University of Science and Technology, School of Computer Science and Technology, 210094, Nanjing, People’s Republic of China
Venue:
Neural Computing and Applications
Year:
2012

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Abstract

Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel planes, i.e., ε-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal solution since its objective function is positive semi-definite and the lack of complexity control. In order to address this shortcoming, we develop a novel SVR algorithm termed as smooth twin SVR (STSVR). The idea is to reformulate TSVR as a strongly convex problem, which results in unique global optimal solution for each subproblem. To solve the proposed optimization problem, we first adopt a smoothing technique to convert the original constrained quadratic programming problems into unconstrained minimization problems, and then use the well-known Newton–Armijo algorithm to solve the smooth TSVR. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-world benchmark datasets.