Adaptive Evolutionary Fusion for Multi-View Clustering

Deep multi-view clustering (MVC) methods achieve impressive performance by effectively capturing complementary information across views, where feature fusion serves as the critical mechanism for maximizing cross-view complementarity. However, most existing methods suffer from rigid dependence on non-adaptive predefined fusion operations, resulting in unverifiable and potentially suboptimal fused feature quality. To resolve these limitations, we propose a novel multi-view clustering framework that learns adaptive hierarchical fusion through an unsupervised evolutionary algorithm. Unlike conventional predefined-fusion strategies, our approach employs tree-structured representations (Fusion Trees) for adaptive feature integration. These Fusion Trees are optimized via our evolutionary mechanism, in which models sharing identical architectures but distinct Fusion Trees are conceptualized as evolutionary individuals. Through implementation of the evolutionarily optimized Fusion Tree, the resultant model generates discriminative representations in accordance with biological evolutionary principles. Comprehensive benchmarking across twelve multi-view datasets validates significant performance gains improvement over state-of-the-art baselines.