Abstract
Semi-supervised multi-view classification plays a crucial role in understanding and utilizing existing multi-view data, especially in domains like medical diagnosis and autonomous driving. However, conventional semi-supervised multi-view classification methods often merely fuse features from multiple views without significantly improving classification performance. To address this issue, we propose a dynamic fusion approach for Semi-supervised Mult I-view c Lassification (SMILE). This approach leverages a high-level semantic mapping module to extract discriminative features from each view, reducing redundancy features. Furthermore, it introduces a dynamic fusion module to assess the quality of different views of different samples dynamically, diminishing the negative impact of low-quality views. We compare our method with six competitive methods on four datasets, exhibiting distinct advantages on the classification task, which demonstrates significant performance improvements across various evaluation metrics. Visualization experiments demonstrate that our approach is able to learn classification-friendly representations.