Hierarchical Residual Guidance for Multi-View Clustering

Deep multi-view clustering (MVC) commonly relies on stacked view-specific encoders to learn increasingly abstract representations. However, deeper encoders do not necessarily produce better clustering features, because repeated nonlinear transformations can progressively weaken useful structures established at earlier stages. We propose Hierarchical Residual Guidance for Multi-View Clustering (HRG-MVC), which organizes deep multi-view representation learning around a hierarchical residual encoder. At each stage, the projected input is retained through an additive shortcut and the nonlinear branch learns only an incremental update, providing an explicit information path across encoder depth. To complement this view-specific residual hierarchy, a Cross-View Transformer estimates level-wise consensus from the representations of the same sample across views. The consensus is projected and selectively introduced into the subsequent residual stage through a feature-wise gate, allowing complementary cross-view information to refine deeper features without replacing their local basis. Reconstruction, inter-view feature contrast, and inter-level assignment contrast jointly optimize the hierarchy. Experiments on six benchmark datasets show competitive clustering performance, while depth and stress-test studies demonstrate the improved stability of residual encoders as the network becomes deeper. Additional ablations characterize the role of Transformer-based consensus and selective cross-view feedback.