Shi Zhibin, Lin Zhenghong, Lin Weihong, Wang Shiping
College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China; Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Wiyishan, 354300, China.
Neural Netw. 2025 Aug;188:107451. doi: 10.1016/j.neunet.2025.107451. Epub 2025 Apr 11.
Graph structure is widely used in the field of multi-view learning. Hypergraph which is a kind of extension of graph can capture the higher-order relationships of nodes in a better way. However, most existing hypergraph-based models are based on the assumption that hypergraph structures are readily available, which is untenable in most cases. In order to alleviate this problem, we propose the learnable unified hypergraph dynamic system framework, a novel approach in unified cross-view hypergraph structure generation tailored for multi-view semi-supervised classification. Specifically, we introduce four strategies for unified cross-view hypergraph generation and propose a mechanism for generating learnable unified cross-view hypergraph. Furthermore, we utilize a dynamic diffusion model to dynamically learn unified hypergraph structure which can achieve better performance in multi-view semi-supervised classification tasks. Extensive experimental results on various real datasets show that the proposed method outperforms other state-of-the-art multi-view algorithms.
图结构在多视图学习领域中被广泛使用。超图作为图的一种扩展，能够以更好的方式捕捉节点的高阶关系。然而，大多数现有的基于超图的模型都基于超图结构 readily available 这一假设，而这在大多数情况下是站不住脚的。为了缓解这个问题，我们提出了可学习的统一超图动态系统框架，这是一种针对多视图半监督分类量身定制的统一跨视图超图结构生成的新方法。具体来说，我们引入了四种用于统一跨视图超图生成的策略，并提出了一种生成可学习的统一跨视图超图的机制。此外，我们利用动态扩散模型来动态学习统一超图结构，该结构在多视图半监督分类任务中能够实现更好的性能。在各种真实数据集上的大量实验结果表明，所提出的方法优于其他现有的多视图算法。
