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Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classification

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Abstract

For semi-supervised learning, a few labeled data and a large number of unlabeled data are used to construct a reasonable classifier. In recent years, many semi-supervised learning methods have been proposed and achieved good performance, especially for the graph-based approaches that can exploit the geometric information embedded in the data. Motivated by the success of generalized elastic net Lp-norm nonparallel support vector machine (GLpNPSVM) and the graph-based regularization term, in this paper, a novel Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised learning (Lap-GLpNPSVM) is proposed. A Lp-norm graph regularization term is introduced to improve the performance by the adjustability of the value of p. Experimental results on two synthetic datasets, fifteen UCI datasets, and Handwritten Numeral datasets demonstrate that proposed methods outperform other state-of-the-art methods.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the archive repository, https://archive.ics.uci.edu/ml/index.php.

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Acknowledgements

This work is supported by NSFC 61906101.

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Authors and Affiliations

  1. The School of Information Science and Engineering, Ningbo University, Zhejiang, 315211, China

    Xijiong Xie & Feixiang Sun

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  1. Xijiong Xie
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  2. Feixiang Sun
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Correspondence to Xijiong Xie.

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Xie, X., Sun, F. Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classification. Neural Comput & Applic 35, 15857–15875 (2023). https://doi.org/10.1007/s00521-023-08548-3

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