DiFuMVC: Distribution Fusion Multi-View Clustering

Multi-view clustering (MVC) must decide how to represent each view, how to fuse information across views, and how to cluster. Most MVC pipelines operate on sample relations, point prototypes, or latent codes. Some additionally align or transport distributions in selected representation or fusion operations, while their final clustering remains sample-level. We introduce DiFuMVC, which adopts a distributional perspective through all three stages. Distributional representation retains $k$ components in each view and encodes each empirical distribution by a kernel mean embedding (KME). Distribution fusion uses shared reference-object identities to compare source-view points with target-view KMEs while preserving view-specific geometry. Distribution-driven clustering assembles all ordered view pairs into an $n\times M(M\le k{V}^2)$ point-distribution bipartite graph and applies transfer cut on its compact distribution side. We prove that the induced cross-view kernel is positive semidefinite, show that point–distribution fusion is an expected cross-view kernel affinity, and derive conditional finite-sample bounds that propagate both KME sampling error and random-reference approximation error through the fused graph to its compact spectral subspace under fixed-rule and split-reference conditions. Benchmark experiments demonstrate the efficiency and effectiveness of our proposed DiFuMVC.