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Fixed-point twin support vector machine

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Abstract

Twin support vector machine and many of its variants proposed recently generate two optimal separating hyperplanes by solving two dual constrained quadratic programming problems (QPPs) independently. However, each dual QPP involves a set of dual variables with its size determined by the number of patterns in the other class. This will lead to prohibitively computational complexity when these models are encountered with large-scale datasets. In this paper, we propose an improved twin support vector machine, termed as fixed-point twin support vector machine, in which each dual QPP in the traditional TWSVM and its variants in high dimensional space is converted into a sequence of successive minimization problems of unimodal functions in one dimension which can be solved by using line search methods like the Fibonacci search method or the golden section rule. Numerical experiments on several benchmark datasets including large-scale datasets are performed to verify the validity of our proposed algorithm. Experimental results indicate that the our model gains faster training speed while maintaining comparable classification accuracy.

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Acknowledgements

This work was supported by the National High Technology Research and Development Program of China(863 Program)(2011AA010706), the National Natural Science Foundation of China (61133016, 61272527), and Ministry of Education-China Mobile Communications Corporation Research Funds (MCM20121041).

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Jiayan Fang, Qiao Liu & Zhiguang Qin

  2. Sichuan Province Key Laboratory of Network and Data Security, Chengdu, China

    Jiayan Fang, Qiao Liu & Zhiguang Qin

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  1. Jiayan Fang
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  2. Qiao Liu
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  3. Zhiguang Qin
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Correspondence to Jiayan Fang.

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Fang, J., Liu, Q. & Qin, Z. Fixed-point twin support vector machine. Cluster Comput 22 (Suppl 4), 7991–8005 (2019). https://doi.org/10.1007/s10586-017-1572-2

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