Transport Barycenter-Guided Sample-to-Cluster Matching for Unaligned Multi-view Clustering

Multi-view clustering (MVC) relies on consistency learning to align and fuse multi-view information for building clustering decision boundaries. However, mainstream methods adopt sample-to-sample/distribution/structure similarity smoothing for consistency alignment, which builds upon continuous cluster manifolds with semantic and geometric overlap and reliable paired priors of sample correspondences. They suffer from semantic- and instance-level view-unaligned problems inherent in the real-world data with discrete non-convex structures and unreliable cross-view correspondences. Misled by unaligned noise, they weaken discriminative semantic boundaries via similarity smoothing over false-positive pairs, and degrade discrete non-convex structures by mistakenly bridging distinct cluster manifolds via pseudo-semantic interpolation. Such consistency alignment pursues highly similar representations, distributions and structures, which deviates from the clustering logic of many-to-one partitioning as well as its learning goal for separable semantic boundaries. Guided by the many-to-one principle, we jointly formulate consistency alignment and clustering decision as a novel sample-to-cluster map, termed Multi-view Discrete Optimal Transport. Specifically, MvDOT is instantiated as a cluster-level transport matching framework that first adopts semi-discrete OT to learn a global OT barycenter via aggregating semantic-geometric information from all views, and then transports samples to barycenter-anchored consensus clusters under consistency constraints on semantic assignment and geometric measure. Even with discrete cluster manifolds and unreliable sample correspondences, MvDOT achieves cross-view consistent alignment while preserving inter-cluster semantic boundaries to uncover the underlying cluster structures. Extensive experiments show MvDOT outperforms 10 baselines with higher confidence and stronger robustness in complex MVC tasks.