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Robust General Twin Support Vector Machine with Pinball Loss Function

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Machine Learning for Intelligent Multimedia Analytics

Part of the book series: Studies in Big Data ((SBD,volume 82))

Abstract

Twin support vector machines (TWSVM) with hinge loss suffer from noise sensitivity and instability. To overcome these issues, pinball loss based general twin support vector machines (Pin-GTSVM) was recently proposed. However, TWSVM and Pin-GTSVM implement the empirical risk minimization principle. Also, the matrices in their dual formulations are positive semi-definite. To overcome these issues, we propose pinball loss based robust general twin support vector machines (Pin-RGTSVM). Pin-RGTSVM implements the structural risk minimization principle which embodies the marrow of statistical learning and pinball loss function makes it more robust for noisy datasets. Also, the matrices appear in the dual formulation of the proposed Pin-RGTSVM are positive definite. The incorporation of the structural risk minimization principle via introduction of the regularisation term leads to the improved generalization performance of the proposed Pin-RGTSVM. Numerical experiments and statistical evaluation on the real world benchmark datasets show the efficacy of the proposed Pin-RGTSVM.

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Acknowledgements

This work is supported by the Council of Scientific & Industrial Research (CSIR), New Delhi (Grant No. 22(0751)/17/EMR-II) and Science and Engineering Research Board (SERB) (Grant Nos. SB/S2/RJN-001/2016, ECR/2017/000053). We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.

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Authors and Affiliations

  1. Indian Institute of Technology Indore, Indore, MP, 453552, India

    M. A. Ganaie & M. Tanveer

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  1. M. A. Ganaie
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  2. M. Tanveer
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Correspondence to M. Tanveer .

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Editors and Affiliations

  1. Department of Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Pardeep Kumar

  2. Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, India

    Amit Kumar Singh

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Ganaie, M.A., Tanveer, M. (2021). Robust General Twin Support Vector Machine with Pinball Loss Function. In: Kumar, P., Singh, A.K. (eds) Machine Learning for Intelligent Multimedia Analytics. Studies in Big Data, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-15-9492-2_6

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