Abstract
In this paper, we propose a novel feature selection method which can suppress the input features during the process of model construction automatically. The main idea is to obtain better performance and sparse solutions by introducing Tikhonov regularization terms and measuring the objective function with \(L_1 \)-norm, based on projection twin support vector machine. Furthermore, to make the problem easy to solve, the exterior penalty theory is adopted to convert the original problem into an unconstrained problem. In contrast with twin support vector machine which needs solve two QPPs, our method only solves two linear equations by using a fast generalized Newton algorithm. In order to improve performance, a recursive algorithm is proposed to generate multiple projection axes for each class. To disclose the feasibility and effectiveness of our method, we conduct some experiments on UCI and Binary Alpha-digits data sets.
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Acknowledgements
This work was supported by the National Science Foundation of China (No. 61401214), the Project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology), the Practice Innovation Training Program Projects for Jiangsu College Students (No. 201610298066Z), and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (No. PPZY2015A062), and the Natural Science Foundation of Jiangsu Province (No. BK20140058).
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Yan, A.R., Ye, B.Q., Zhang, C.L. et al. A Feature Selection Method for Projection Twin Support Vector Machine. Neural Process Lett 47, 21–38 (2018). https://doi.org/10.1007/s11063-017-9624-4
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DOI: https://doi.org/10.1007/s11063-017-9624-4