Robust multi-view clustering via adaptive fusion and disentangled representation learning

Multi-view clustering aims to fully explore the consistency and complementarity of multi-view data and has attracted increasing attention in recent years. However, most existing methods generally treat all views equally during the view fusion process, which cannot adaptively adjust the weights in terms of the quality and contribution of each view. Meanwhile, the disentanglement performance of those methods between view-common attribute and view-specific attributes is inadequate, thereby restricting the expressiveness of the learned features. To tackle these challenges, a novel framework of robust multi-view clustering via adaptive fusion and disentangled representation learning (AFDR-MVC) is proposed. Firstly, a multi-head attention mechanism is introduced to model inter-view correlations, which facilitates interaction and alignment among multiple views. Subsequently, a learnable gating network is employed to adaptively assign weights to each view based on its importance, allowing the model to learn a discriminative common representation under significant variations in view quality and noise. Finally, a mutual information minimization constraint is introduced to explicitly disentangle view-common variable and view-specific variables within a variational inference framework, yielding more discriminative common representation and specific representation which can improve clustering accuracy. Experimental results on multi-view datasets demonstrate that the proposed AFDR-MVC achieves superior clustering performance.