An Interior Point Method for L1/2-SVM and Application to Feature Selection in Classification. (10th April 2014)

• Record Type: Journal Article
• Authors: Yao, Lan; Zhang, Xiongji; Li, Dong-Hui; Zeng, Feng; Chen, Haowen
• Other Names: Werner Frank Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2014(2014)
• Publication Date: 2014-04-10
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2014/942520
• Languages: English
• ISSNs: 1110-757X

Abstract

Abstract : This paper studies feature selection for support vector machine (SVM). By the use of theL 1 / 2 regularization technique, we propose a new modelL 1 / 2 -SVM. To solve this nonconvex and non-Lipschitz optimization problem, we first transform it into an equivalent quadratic constrained optimization model with linear objective function and then develop an interior point algorithm. We establish the convergence of the proposed algorithm. Our experiments with artificial data and real data demonstrate that theL 1 / 2 -SVM model works well and the proposed algorithm is more effective than some popular methods in selecting relevant features and improving classification performance.