Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification

• URL: http://arxiv.org/abs/2303.17117v2
• Date: Mon, 26 Aug 2024 03:22:08 GMT
• Title: Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification
• Authors: Chengliang Liu, Jie Wen, Yong Xu, Bob Zhang, Liqiang Nie, Min Zhang,
• Abstract summary: In this paper, we propose an incomplete multi-view partial multi-label classification network named RANK. We break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample. Our model is not only able to handle complete multi-view multi-label datasets, but also works on datasets with missing instances and labels.
• Score: 78.15629210659516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely separate the so-called negative pair, which largely results in the separation of samples belonging to the same category or similar ones. Besides, plenty of multi-view multi-label learning methods ignore the possible absence of views and labels. To address these issues, in this paper, we propose an incomplete multi-view partial multi-label classification network named RANK. In this network, a label-driven multi-view contrastive learning strategy is proposed to leverage supervised information to preserve the structure within view and perform consistent alignment across views. Furthermore, we break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample. The label correlation information is fully utilized in the final multi-label cross-entropy classification loss, effectively improving the discriminative power. Last but not least, our model is not only able to handle complete multi-view multi-label datasets, but also works on datasets with missing instances and labels. Extensive experiments confirm that our RANK outperforms existing state-of-the-art methods.

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