SparseMVC: Probing Cross-view Sparsity Variations for Multi-view Clustering
Ruimeng Liu, Xin Zou, Chang Tang, Xiao Zheng, Xingchen Hu, Kun Sun, Xinwang Liu
Existing multi-view clustering methods employ various strategies to address dataset-level sparsity and view-level dynamic fusion. However, we identify a critical yet overlooked issue: the varying sparsity across views.  Cross-view sparsity variations lead to encoding discrepancies, heightening sample-level semantic heterogeneity and making view-level dynamic weighting inappropriate. To tackle these challenges, we propose Adaptive Sparse Autoencoders for Multi-View Clustering (SparseMVC), a framework with three key modules. Initially, the Sparse Autoencoder probes the sparsity of each view and adaptively adjusts encoding formats via an entropy-matching loss term, mitigating cross-view inconsistencies. Subsequently, the Correlation-Informed Sample Reweighting module employs attention mechanisms to assign weights by capturing correlations between early-fused global and view-specific features, reducing encoding discrepancies and balancing contributions. Furthermore, the Cross-view Distribution Alignment module aligns feature distributions during the late fusion stage, accommodating datasets with an arbitrary number of views. Extensive experiments demonstrate that our proposed method achieves state-of-the-art clustering performance. Overall, our framework advances the field by extending sparsity handling from the dataset level to the view level and mitigating the adverse effects of encoding discrepancies through sample-level dynamic weighting. The source code will be public upon acceptance.