Releasing Anchors from Cross-View Correspondence: Probabilistic Multi-View Anchor Graph Clustering

Anchor graphs are a standard route to scalable multi-view clustering, but most existing pipelines still depend on either a shared anchor set sampled from concatenated features or a separate anchor-alignment stage before fusion. Both choices are restrictive: the former hard-codes equal anchor budgets and importance at anchor-construction time, while the latter introduces a cumbersome correspondence problem and still struggles with unequal anchor numbers. We develop an alignment-free probabilistic formulation in which view-specific anchor graphs are noisy manifestations of a shared latent cluster identity rather than objects that require cross-view correspondence. Each view has its own anchor space, number of anchors and cluster prototypes; the only shared latent variable is the sample label. We instantiate this idea with a Gaussian latent-label model and a deterministic prototype M-step. The resulting variational EM algorithm admits closed-form updates with convergence guarantees, while experiments demonstrate competitive clustering performance and practical runtime on most datasets.