Journal of Applied Mathematics

An Interior Point Method for L 1 / 2 -SVM and Application to Feature Selection in Classification

Lan Yao, Xiongji Zhang, Dong-Hui Li, Feng Zeng, and Haowen Chen

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This paper studies feature selection for support vector machine (SVM). By the use of the L 1 / 2 regularization technique, we propose a new model L 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 the L 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.

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J. Appl. Math., Volume 2014 (2014), Article ID 942520, 16 pages.

First available in Project Euclid: 2 March 2015

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Yao, Lan; Zhang, Xiongji; Li, Dong-Hui; Zeng, Feng; Chen, Haowen. An Interior Point Method for ${L}_{1/2}$ -SVM and Application to Feature Selection in Classification. J. Appl. Math. 2014 (2014), Article ID 942520, 16 pages. doi:10.1155/2014/942520.

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