Mutually Reinforced Fair Multi-View Clustering

摘要

Fair multi-view clustering aims to exploit complementary information across views to improve clustering quality, while ensuring unbiased outcomes with respect to sensitive groups. Existing methods typically separate multi-view clustering from fairness optimization into distinct stages. However, such a decoupled design provides limited synergy between the two objectives, which often makes progress on one objective detrimental to the other. To address this challenge, we propose Mutually Reinforced Fair Multi-View Clustering (MR-FMVC), which optimizes fairness alignment and clustering iteratively to enable coordinated optimization. Specifically, we first disentangle view representations into common and private components to mitigate fairness-related discrepancies across views. Subsequently, the optimization proceeds by interleaving cross-group matching in the common space with updating cluster centroids in the joint representation space, ultimately assigning matched samples to consistent clusters. Extensive experiments on four fairness datasets demonstrate that MR-FMVC achieves a superior trade-off between clustering performance and fairness.