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Multi-View Partial Multi-Label Learning via Class Activation Specific Features Collaborative Learning
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AI
DOI：10.1109/TETC.2025.3629677.png)
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Abstract
En 中文
Multi-view partial multi-label learning deals with scenarios where samples contain heterogeneous features and are associated with both relevant and corrupted labels. Existing methods struggle to effectively capture label-related features through adequate feature interaction while simultaneously integrating inter- and intra-view features. To address these challenges, we propose a robust and scalable framework, Class Activation Specific Features Collaborative Network, designed to handle feature heterogeneity and facilitate comprehensive feature fusion in multi-view partial multi-label learning. The framework integrates label-specific feature extraction with collaborative information propagation through two key components: 1) View-Specific Class Activation Map, which transforms multi-view features into compact class label representations and 2) Class Information Propagation Correction, which refines and propagates accurate class label information by leveraging graph convolutional networks and transformers. Additionally, we introduce a multi-faceted loss function that promotes robust feature learning and architectural stability via consistency-based structural loss, while improving generalization through knowledge distillation. Extensive experiments on benchmark datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods in multi-view partial multi-label learning tasks.
Keywords:
Class activation
graph convolutional network
multi-view data
noise robustness
partial multi-label learning
transformer
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