Dual Kernel Contrastive Learning for Partially-Aligned Multi-View Clustering

Although multi-view clustering has achieved significant progress, most existing methods assume that all views are fully aligned. In real-world scenarios, however, temporal asynchrony, spatial misalignment, and heterogeneous acquisition condi- tions often lead to partially aligned views, posing substantial challenges to effec- tive clustering. Existing partially-aligned multi-view clustering methods mainly suffer from two limitations: they either insufficiently exploit abundant unaligned samples or rely on cumbersome and error-prone explicit sample-level realign- ment, which may lead to suboptimal clustering performance. To address these issues, we propose a simple yet effective method, termed Dual Kernel Contrastive Learning for Partially-Aligned Multi-View Clustering (DKLPVC). Specifically, DKLPVC first exploits cross-view semantic similarity matrices computed from aligned samples to alleviate view distribution shifts and further uses them as prior information to construct a kernel alignment graph. This graph serves as a seman- tic bridge between sparse aligned pairs and abundant unaligned samples, thereby avoiding explicit sample-level realignment. Under the guidance of the learned graph, DKLPVC performs kernel contrastive learning at both the feature and clus- ter levels in a kernel-induced Hilbert space. The feature-level objective enhances local cross-view semantic consistency, while the cluster-level objective corrects local semantic bias from a global clustering perspective by encouraging intra- cluster compactness and inter-cluster separation. Extensive experiments on eight benchmark datasets demonstrate that DKLPVC consistently outperforms existing methods in partially-aligned multi-view clustering tasks.