Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework

• URL: http://arxiv.org/abs/2411.18267v1
• Date: Wed, 27 Nov 2024 12:04:04 GMT
• Title: Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework
• Authors: Bingyan Nie, Wulin Xie, Jiang Long, Xiaohuan Lu,
• Abstract summary: In this paper, we propose our dual-level contrastive learning framework to solve the issue of double missing multi-view multi-label classification. Experiments on several widely used benchmark datasets demonstrate that the proposed method has more stable and superior classification performance.
• Score: 1.224954637705144
• License:
• Abstract: Recently, multi-view and multi-label classification have become significant domains for comprehensive data analysis and exploration. However, incompleteness both in views and labels is still a real-world scenario for multi-view multi-label classification. In this paper, we seek to focus on double missing multi-view multi-label classification tasks and propose our dual-level contrastive learning framework to solve this issue. Different from the existing works, which couple consistent information and view-specific information in the same feature space, we decouple the two heterogeneous properties into different spaces and employ contrastive learning theory to fully disentangle the two properties. Specifically, our method first introduces a two-channel decoupling module that contains a shared representation and a view-proprietary representation to effectively extract consistency and complementarity information across all views. Second, to efficiently filter out high-quality consistent information from multi-view representations, two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. Extensive experiments on several widely used benchmark datasets demonstrate that the proposed method has more stable and superior classification performance.

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