Abstract
Multi-view clustering (MVC) has emerged as a leading paradigm in unsupervised learning, gaining significant attention. Central to this framework is the concept of view-pair contrastive learning, which aims to maximize mutual information between pairs of views, thereby facilitating consistent latent representations. Nevertheless, two critical challenges remain: i) Identifying the most suitable pairs of views for contrastive learning becomes difficult when more than two views are available, especially in the absence of prior knowledge; ii)Including all available views in contrastive learning can degrade performance due to the presence of low-quality views. To address these issues, we propose a novel mechanism, EASEMVC(Efficient DuAl Selection MEchanism for Deep Multi-View Clustering). EASEMVC begins by constructing a view graph using the Optimal Transport (OT) distance between bipartite graphs of individual views. A view selection module is then designed to perform efficient view-level selection based on the topological relationships within the view graph. Additionally, a cross-view sample graph is built at the sample level, where the topological relationships among samples are used to generate reliable learning weights. Leveraging the selected view pairs and sample weights, contrastive learning is employed to obtain consistent representations across views. Extensive experiments across six benchmark datasets demonstrate that EASEMVC outperforms current state-of-the-art methods.