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On Lagrangian twin support vector regression

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Abstract

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability.

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Acknowledgments

The authors are extremely thankful to the learned referees for their critical and constructive comments that greatly improved the earlier version of the paper. Mr. Tanveer acknowledges the financial support given as scholarship by Council of Scientific and Industrial Research, India.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    S. Balasundaram & M. Tanveer

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  1. S. Balasundaram
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  2. M. Tanveer
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Correspondence to S. Balasundaram.

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Balasundaram, S., Tanveer, M. On Lagrangian twin support vector regression. Neural Comput & Applic 22 (Suppl 1), 257–267 (2013). https://doi.org/10.1007/s00521-012-0971-9

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  • DOI: https://doi.org/10.1007/s00521-012-0971-9

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