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| Semi-supervised multi-view similarity and discriminative representation joint learning |
Yaru HAO1( ),Yuangao AI1,Peng ZHANG1,Wei WU2,*() |
1. Three Gorges Hydropower Plant, China Yangtze Power Limited Company, Yichang 443134, China
2. Hainan Institute, Zhejiang University, Sanya 572025, China |
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Abstract A semi-supervised multi-view similarity and discriminative representation joint learning method (SMLSDR) was proposed aiming at the problem that existing semi-supervised multi-view learning methods fail to fully mine complementary information among different views. In the labeled space, a deep view-specific representation learning network was constructed to fully explore and utilize complementary information across views. In the unlabeled space, a local structure-based cross fusion strategy of similarity matrices was adopted to fully excavate and use inter-view complementary information. Spectral clustering was employed to design a label propagation strategy in order to realize semi-supervised learning. Extensive experiments were conducted on five multi-view datasets in the field of machine learning to verify the effectiveness of SMLSDR. The experimental results demonstrated that the proposed SMLSDR outperformed baseline methods. Further ablation experiments prove that both deep discriminative representation learning in labeled space and similarity matrix fusion in unlabeled space contribute positively to semi-supervised classification.
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Received: 24 September 2025
Published: 20 July 2026
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| Fund: 国家自然科学基金资助项目（62561057，62566020）；浙江大学自主部署启动项目(6602-A12202). |
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Corresponding Authors:
Wei WU
E-mail: hyr2018@whu.edu.cn;wuweiux@zju.edu.cn
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半监督多视图相似性和鉴别表示联合学习
针对现有半监督多视图学习方法视图间互补信息挖掘不充分的问题，提出半监督多视图相似性和鉴别表示联合学习方法（SMLSDR）. 在标签空间，通过构建各视图深度特定表示学习网络来充分挖掘和利用视图间的互补信息.在无标签空间，采用基于局部结构的相似矩阵交叉融合策略来充分挖掘和利用视图间的互补信息. 利用谱聚类设计标签传播策略，实现半监督学习. 在机器学习领域5个多视图数据上进行大量的实验，验证SMLSDR的有效性. 实验结果表明，所提方法SMLSDR的性能优于基线方法．进一步的消融实验证明，标签空间的深度鉴别表示学习和无标签空间的相似矩阵融合都对半监督分类有一定的贡献.
关键词：
多视图表示学习,
半监督学习,
鉴别信息,
结构信息,
谱聚类
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