Reliability-Aware Gaussian Residual Counterpart Generation for Robust Multi-View Clustering with Noisy Correspondence

Multi-view clustering (MvC) aims to discover cluster structures by exploiting complementary information across views. Most existing MvC methods assume that same-index observations across views describe the same semantic instance. In practice, however, index-aligned observations can be semantically unrelated. This inconsistency between observed index-level correspondence and underlying semantic correspondence is known as noisy correspondence (NC). Learning from such mismatched pairs imposes erroneous cross-view constraints and distorts clustering. Many existing methods only suppress unreliable pairs. This discriminative strategy avoids incorrect alignment but also excludes suspicious pairs from cross-view learning. To reuse these pairs without enforcing incorrect correspondence, we propose Reliability-Aware Gaussian Residual Counterpart Generation. Using reliability estimates derived from cross-view losses, the framework retains observed counterparts for reliable pairs and routes unreliable pairs to counterpart generation. For each unreliable pair, prototype-level semantic transport locates a matched target-view prototype. A Gaussian residual model estimated from reliable target-view samples captures variations around this prototype. The framework samples a residual from this model and adds it to the prototype center, yielding a semantically matched yet diverse counterpart. Random walk-based intra-view contrastive learning further preserves neighborhood structures. Experiments on Scene15, LandUse21, Reuters, and CCV20 achieve the best average ACC, NMI, and ARI across the evaluated NC ratios. Ablation and transfer studies further support the effectiveness of the proposed design.