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Robust nonparallel support vector machine with privileged information for pattern recognition

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Abstract

In the field of pattern recognition, collected data always include some additional information which are usually termed as privileged information. The privileged information is latent information belonging to the training samples, which can be easily ignored. The privileged information can help to build a better classifier for classification. In this paper, we try to construct a robust nonparallel support vector machine (NPSVM) model under the privileged information learning (LUPI) setting, termed as R-NPSVM+. On the one hand, we introduce the privileged information into NPSVM so as to build a model for classification. In the training process, both the privileged information and “usual” samples are used to train the model, which can enhance the accuracy. On the other hand, due to the ε-insensitive loss and hinge loss, NPSVM is sensitive to noise or outliers. Hence, we use two robust loss functions in R-NPSVM+ model, which can further ensure the robustness of the model. In addition, we use the Lagrange multiplier method and the dual coordinate descent (DCD) algorithm to optimize the proposed objective function, respectively. Lastly, to evaluate the performance of R-NPSVM+, we conduct a series of experiments. Experimental results confirm that compared with other classical SVM-type algorithms, our R-NPSVM+ can produce a better performance, especially when the samples are corrupted by noise and outliers.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants (61673199), Natural Science Foundation of Liaoning Province of China (2022-MS-353), Basic Scientific Research Project of Education Department of Liaoning Province (2020LNZD06 and LJKMZ20220640), University of Science and Technology Liaoning Talent Project Grants (601011507–20), University of Science and Technology Liaoning Team Building Grants (601013360–17) and Graduate Science and Technology Innovation Program of University of Science and Technology Liaoning (LKDYC201909).

Funding

National Natural Science Foundation of China, 61673199, Ping Li.

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  1. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, China

    Liming Liu, Ping Li, Maoxiang Chu & Shuming Liu

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  1. Liming Liu
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  2. Ping Li
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  4. Shuming Liu
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Correspondence to Ping Li or Maoxiang Chu.

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Liu, L., Li, P., Chu, M. et al. Robust nonparallel support vector machine with privileged information for pattern recognition. Int. J. Mach. Learn. & Cyber. 14, 1465–1482 (2023). https://doi.org/10.1007/s13042-022-01709-1

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