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Multi-view learning methods with the LINEX loss for pattern classification
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DOI：10.1016/j.knosys.2021.107285.png)
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Abstract
En 中文
Multi-view learning concentrates on leveraging the consensus and complementarity information among multiple distinct feature representations to improve the performance. Most multi-view learning models deal with two main issues. Firstly, how to fully exploit the view-agreement and view-discrepancy poses a major challenge. Secondly, how to design a general multi-view model is indispensable. By inheriting the asymmetric merit of LINEX loss, we propose a general multi-view LINEX SVM framework, which includes two models called MVLSVM-CO and MVLSVM-SIM. They can not only use LINEX loss function to flexibly distinguish the error-prone samples of both classes, but also take advantage of the consistency and the complementarity of distinct views in multi-view scenario. An iterative two-step strategy is adopted to solve the optimization problems efficiently. Furthermore, we theoretically analyze the view-consistency and generalization capability of the proposed models by using Rademacher complexity. The extensive experiments confirm the effectiveness of MVLSVM-CO and MVLSVM-SIM. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Multi-view learning
Consensus and complementarity information
Asymmetric LINEX loss function
Support vector machine
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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