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1-Norm Projection Twin Support Vector Machine

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

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Abstract

In this paper, we propose a novel feature selection method which can suppress the input features automatically. We first introduce a Tikhonov regularization term to the objective function of projection twin support vector machine (PTSVM). Then we convert it to a linear programming (LP) problem by replacing all the 2-norm terms in the objective function with 1-norm ones. Then we construct an unconstrained convex programming problem according to the exterior penalty (EP) theory. Finally, we solve the EP problems by using a fast generalized Newton algorithm. In order to improve performance, we apply a recursive algorithm 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 Alphadigits data sets.

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Acknowledgement

This work was supported in part by the National Foundation for Distinguished Young Scientists under Grant 31125008, in part by the Scientific Research Foundation for Advanced Talents and Returned Overseas Scholars of Nanjing Forestry University, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions, China, under Grant 14KJB520018, in part by the Practice Innovation Training Program Projects for Jiangsu College Students under Grant 2015sjcx119, and in part by the National Science Foundation of China under Grant 61101197 and Grant 61401214.

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Authors and Affiliations

  1. School of Information Science and Technology, Nanjing Forestry University, Nanjing, China

    Rui Yan, Qiaolin Ye, Dong Zhang, Ning Ye & Xiaoqian Li

Authors
  1. Rui Yan
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  2. Qiaolin Ye
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  3. Dong Zhang
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  4. Ning Ye
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  5. Xiaoqian Li
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Corresponding author

Correspondence to Qiaolin Ye .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Yan, R., Ye, Q., Zhang, D., Ye, N., Li, X. (2016). 1-Norm Projection Twin Support Vector Machine. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_44

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

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