Abstract
Multi-view classification methods have better generalization performance compared to the single-view classification methods due to the consistency information from multiple views. In recent years, the combination of support vector machine (SVM) and multi-view learning has been widely studied. To improve the robustness of multi-view classification methods, emphasis has shifted to the integration of multi-view classification approaches with fully-connected and convolutional neural networks. A classical deep two-view classification method named deep SVM-2K is a combination of support vector machine with two stage kernel canonical correlation analysis (SVM-2K) and deep learning. However, limitations of deep SVM-2K are that it can not cope with multi-view classification and multiclass classification problems. To address these issues, we propose two novel deep multi-view models named deep multi-view support vector machine (DMVSVM) for multiclass classification. DMVSVM uses the learned features by auto-encoder (AE) or deep neural network (DNN) to train the SVM classifier for each view. The two models then impose some constraints to make the output of the multi-view SVM classifiers as consistent as possible, which used to exploring intrinsic relations. Experiments performed on different real-word datasets show the effectiveness of our proposed approaches.
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Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, [Xj Xie], upon reasonable request.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No.61906101). It is also supported by the Ningbo Municipal Natural Science Foundation of China (No. 2023J115).
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\(\bullet \)Yanfeng Li: Conceptualization, Data Curation, Methodology, Visualization, Writing-Original Draft, Writing-Review and Editing.\(\bullet \)Xijiong Xie: Conceptualization, Funding Acquisition, Writing-Re-view and Editing.
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Li, Y., Xie, X. Two novel deep multi-view support vector machines for multiclass classification. Appl Intell 55, 182 (2025). https://doi.org/10.1007/s10489-024-06126-1
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DOI: https://doi.org/10.1007/s10489-024-06126-1