Incomplete Multi-View Multi-Label Learning via Disentangled Representation and Label Semantic Embedding
In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations from different data sources and to fully exploit the missing label information. However, most previous approaches ignore the separation prob...
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| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 30722 - 30731 |
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10.06.2025
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| ISSN: | 1063-6919 |
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| Abstract | In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations from different data sources and to fully exploit the missing label information. However, most previous approaches ignore the separation problem between view-shared and specific information. To address this problem, in this paper, we propose a method that can separate view-consistent features from view-specific features under the Variational Autoen-coder (VAE) framework. Specifically, we first introduce cross-view reconstruction to capture view-consistent features and extract shared information from different views through unsupervised pre-training. Subsequently, we develop a disentangling module to learn specific features by minimizing the variational upper bound of mutual information between consistent and specific features. Finally, we utilize prior label relevance information derived from training data to guide the learning of the distribution of label semantic embeddings, aggregating relevant semantic embeddings and maintaining the label relevance topology in the semantic space. In extensive experiments, our model outperforms existing state-of-the-art algorithms on several real-world datasets, which fully validates its strong adaptability to missing views and labels. |
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| AbstractList | In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations from different data sources and to fully exploit the missing label information. However, most previous approaches ignore the separation problem between view-shared and specific information. To address this problem, in this paper, we propose a method that can separate view-consistent features from view-specific features under the Variational Autoen-coder (VAE) framework. Specifically, we first introduce cross-view reconstruction to capture view-consistent features and extract shared information from different views through unsupervised pre-training. Subsequently, we develop a disentangling module to learn specific features by minimizing the variational upper bound of mutual information between consistent and specific features. Finally, we utilize prior label relevance information derived from training data to guide the learning of the distribution of label semantic embeddings, aggregating relevant semantic embeddings and maintaining the label relevance topology in the semantic space. In extensive experiments, our model outperforms existing state-of-the-art algorithms on several real-world datasets, which fully validates its strong adaptability to missing views and labels. |
| Author | Yan, Xu Yin, Jun Wen, Jie |
| Author_xml | – sequence: 1 givenname: Xu surname: Yan fullname: Yan, Xu email: yanxu@stu.shmtu.edu.cn organization: Shanghai Maritime University – sequence: 2 givenname: Jun surname: Yin fullname: Yin, Jun email: junyin@shmtu.edu.cn organization: Shanghai Maritime University – sequence: 3 givenname: Jie surname: Wen fullname: Wen, Jie email: jiewen_pr@126.com organization: Harbin Institute of Technology,Shenzhen |
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| Snippet | In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations... |
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| SubjectTerms | Computer vision Data mining Disentangled representation learning Feature extraction Mutual information Semantics Soft sensors Topology Training data Upper bound |
| Title | Incomplete Multi-View Multi-Label Learning via Disentangled Representation and Label Semantic Embedding |
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