Rethinking View Consistency: Consensus-Oriented Representation Learning for Multi-View Clustering

Multi-view clustering aims to integrate heterogeneous observations from multiple views to learn a discriminative consensus representation for clustering. Existing methods often place cross-view consistency at the center of representation learning, either by directly aligning different views or by using a global target to constrain individual-view representations. While effective when clustering-relevant information is redundantly distributed across views, such consistency-oriented strategies can become restrictive when discriminative evidence is available only in a subset of views. Enforcing cross-view consistency may weaken view-specific informative structures, while global-to-local constraints may force individual views to reproduce semantics beyond their own observational scope. In this work, we rethink the role of view consistency and propose a consensus-oriented representation learning method for multi-view clustering. Instead of explicitly driving individual views toward cross-view consistency, we preserve their intrinsic relational structures and allow them to contribute differently to a jointly learned consensus. Direct clustering supervision is imposed only on the consensus representation, allowing each view to be optimized according to its contribution to the global clustering structure rather than being forced to match the complete global target. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method effectively preserves view relations and yields more discriminative clustering results. Code will be available upon acceptance.