research-article

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

Authors:

Juan XuAuthors Info & Claims

Pattern Analysis and Applications, Volume 23, Issue 1

Pages 1 - 13

https://doi.org/10.1007/s10044-018-0728-x

Published: 01 February 2020 Publication History

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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Cited By

Xiao YLiu JWen KLiu BZhao LKong X(2022)A least squares twin support vector machine method with uncertain dataApplied Intelligence10.1007/s10489-022-03897-353:9(10668-10684)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-03897-3
Yadav RAbhishek Yadav VVerma SVenkatesan S(2021)HSIC-based affinity measure for learning on graphsPattern Analysis & Applications10.1007/s10044-021-01014-724:4(1667-1683)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s10044-021-01014-7
Zheng XZhang LYan L(2020)Feature Selection Using Sparse Twin Support Vector Machine with Correntropy-Induced LossKnowledge Science, Engineering and Management10.1007/978-3-030-55130-8_38(434-445)Online publication date: 28-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-55130-8_38

Index Terms

Locality preserving projection least squares twin support vector machine for pattern classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Pattern Analysis & Applications

Pattern Analysis & Applications Volume 23, Issue 1

Feb 2020

492 pages

ISSN:1433-7541

EISSN:1433-755X

Issue’s Table of Contents

© Springer-Verlag London Ltd., part of Springer Nature 2018.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2020

Accepted: 18 July 2018

Received: 16 March 2015

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National Natural Science Foundation of China

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Cited By

Xiao YLiu JWen KLiu BZhao LKong X(2022)A least squares twin support vector machine method with uncertain dataApplied Intelligence10.1007/s10489-022-03897-353:9(10668-10684)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-03897-3
Yadav RAbhishek Yadav VVerma SVenkatesan S(2021)HSIC-based affinity measure for learning on graphsPattern Analysis & Applications10.1007/s10044-021-01014-724:4(1667-1683)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s10044-021-01014-7
Zheng XZhang LYan L(2020)Feature Selection Using Sparse Twin Support Vector Machine with Correntropy-Induced LossKnowledge Science, Engineering and Management10.1007/978-3-030-55130-8_38(434-445)Online publication date: 28-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-55130-8_38

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