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One novel class of Bézier smooth semi-supervised support vector machines for classification

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Abstract

The semi-supervised support vector machine (S3VM) for classification is introduced for dealing with quantities of unlabeled data in the real world. Labeled data are utilized to train the algorithm and then were adapted to classify the unlabeled data. However, this algorithm has several drawbacks, such as the non-smooth term of semi-supervised objective function negatively affects the classification precision. Moreover, it is required to endure heavy burden in solving two quadratic programming problems with inversion matrix operation. To cope with this problem, this article puts forward a novel class of Bézier smooth semi-supervised support vector machines (BS4VMs), based on the approximation property of Bézier function to the non-smooth term. Because of this approximation, a fast quasi-Newton method for solving BS4VMs can be used to decrease the calculating time scale. This new kind of algorithm enhances the generalization and robustness of S3VM for nonlinear case as well. Further, to show how the BS4VMs can be practically implemented, experiments on synthetic, UCI dataset, USPS dataset, and large-scale NDC database are offered. The theoretical analysis and experiments comparisons clearly confirm the superiority of BS4VMs in both classification accuracy and calculating time.

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Notes

  1. The UCI dataset can be available at https://archive.ics.uci.edu/ml/ datasets.php and https://cs.nyu.edu/~roweis/data.html.

  2. http://archive.ics.uci.edu/ml/index.php.

  3. The USPS datasets are available at http://www.cs.nyu.edu/*roweis/data.html.

  4. http://www.cs.cornell.edu/~arb/data/NDC-classes/.

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Acknowledgements

This work was supported by the Social Science Foundation of China under Grant (18ZDA027).

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Authors and Affiliations

  1. School of Marxism, Xi’an Shiyou University, Xi`an, 710065, China

    En Wang

  2. Research Institute of Frontier Science, Beihang University, Beijing, 100191, China

    Zi-Yang Wang

  3. School of Automation, Xi’an University of Posts and Telecommunications, Xi`an, 710121, China

    Qing Wu

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  1. En Wang
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Correspondence to En Wang or Zi-Yang Wang.

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Wang, E., Wang, ZY. & Wu, Q. One novel class of Bézier smooth semi-supervised support vector machines for classification. Neural Comput & Applic 33, 9975–9991 (2021). https://doi.org/10.1007/s00521-021-05765-6

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