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Locality preserving projection least squares twin support vector machine for pattern classification

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Abstract

During the last few years, multiple surface classification algorithms, such as twin support vector machine (TWSVM), least squares twin support vector machine (LSTSVM) and least squares projection twin support vector machine (LSPTSVM), have attracted much attention. However, these algorithms did not consider the local geometrical structure information of training samples. To alleviate this problem, in this paper, a locality preserving projection least squares twin support vector machine (LPPLSTSVM) is presented by introducing the basic idea of the locality preserving projection into LSPTSVM. This method not only inherits the ability of TWSVM, LSTSVM and LSPTSVM for pattern classification, but also fully considers the local geometrical structure between samples and shows the local underlying discriminatory information. Experimental results conducted on both synthetic and real-world datasets illustrate the effectiveness of the proposed LPPLSTSVM method.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61702012 and 61672265), the University Outstanding Young Talent Support Project of Anhui Province of China (Grant No. gxyq2017026) and the University Natural Science Research Project of Anhui Province of China (Grant Nos. KJ2016A431, KJ2017A361 and KJ2017A368).

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Authors and Affiliations

  1. School of Mathematics and Computational Science, Anqing Normal University, Anqing, 246133, Anhui, People’s Republic of China

    Su-Gen Chen & Juan Xu

  2. School of IoT Engineering, Jiangnan University, Wuxi, 214122, Jiangsu, People’s Republic of China

    Xiao-Jun Wu

  3. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Xiao-Jun Wu

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  1. Su-Gen Chen
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  2. Xiao-Jun Wu
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  3. Juan Xu
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Correspondence to Xiao-Jun Wu.

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Chen, SG., Wu, XJ. & Xu, J. Locality preserving projection least squares twin support vector machine for pattern classification. Pattern Anal Applic 23, 1–13 (2020). https://doi.org/10.1007/s10044-018-0728-x

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