View-Association-Guided Dynamic Multi-View Classification

In multi-view classification tasks, integrating information from multiple views effectively is crucial for improving model performance. However, most existing methods fail to fully leverage the complex relationships between views, often treating them independently or using static fusion strategies. In this paper, we propose a View-Association-Guided Dynamic Multi-View Classification method (AssoDMVC) to address these limitations. Our approach dynamically models and incorporates the relationships between different views during the classification process. Specifically, we introduce a view-relation-guided mechanism that captures the dependencies and interactions between views, allowing for more flexible and adaptive feature fusion. This dynamic fusion strategy ensures that each view contributes optimally based on its contextual relevance and the inter-view relationships. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms traditional multi-view classification techniques, offering a more robust and efficient solution for tasks involving complex multi-view data.

Authors

• Xinyan Liang Shanxi University
• Li Lv Shanxi University
• Qian Guo Taiyuan University of Science and Technology
• Bingbing Jiang Hangzhou Normal University
• Feijiang Li Shanxi University Key Laboratory of Evolutionary Science Intelligence ofShanxi Province
• Liang Du Shanxi University Key Laboratory of Evolutionary Science Intelligence ofShanxi Province
• Lu Chen Shanghai Jiao Tong University Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center

Keywords

• Machine Learning: ML: Classification
• Machine Learning: ML: Multi-modal learning
• Machine Learning: ML: Multi-view learning