research-article

Generalized multi-view learning based on generalized eigenvalues proximal support vector machines

Authors:

Yujie XiongAuthors Info & Claims

Volume 194, Issue C

https://doi.org/10.1016/j.eswa.2021.116491

Published: 15 May 2022 Publication History

Abstract

Multi-view learning based on generalized eigenvalue proximal support vector machines has brought enormous success by mining the consistency information of two views. Nevertheless, it only aims to handle two-view cases and cannot handle generalized multi-view learning cases (above two views). It also omits the complementarity information among views. In this paper, two generalized multi-view extensions of generalized eigenvalue proximal support vector machines are presented which take advantage of the multi-view co-regularization term to mine the consistency information and the weighted value to mine complementarity information. Experimental results performed on synthetic and real world datasets demonstrate that they can provide higher performance than the relevant two-view classification algorithms.

Highlights

•

We propose two new general multi-view learning models.

•

They fully utilize the consensus and complementarity principle among views.

•

They are extended to nonlinear cases by the kernel trick.

•

An iterative algorithm is developed to obtain the solution.

•

Experimental results validate the effectiveness of the proposed methods.

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Cited By

Chen RYang ZYe J(2023)Kernel-Free Nonlinear Support Vector Machines for Multiview Binary Classification ProblemsInternational Journal of Intelligent Systems10.1155/2023/62590412023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6259041
Si QYang ZYe J(2023)Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithmNeural Networks10.1016/j.neunet.2023.08.055168:C(143-160)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.neunet.2023.08.055

Index Terms

Generalized multi-view learning based on generalized eigenvalues proximal support vector machines
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Information systems
  1. Information systems applications
    1. Data mining

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

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 194, Issue C

May 2022

527 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 May 2022

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Cited By

Chen RYang ZYe J(2023)Kernel-Free Nonlinear Support Vector Machines for Multiview Binary Classification ProblemsInternational Journal of Intelligent Systems10.1155/2023/62590412023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6259041
Si QYang ZYe J(2023)Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithmNeural Networks10.1016/j.neunet.2023.08.055168:C(143-160)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.neunet.2023.08.055

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