Deep multi-view subspace clustering via hierarchical diversity optimization of consensus learning

Abstract

Deep multi-view subspace clustering outperforms classic multi-view clustering methods due to its powerful nonlinear feature extraction capabilities. Nevertheless, current deep multi-view clustering approaches face several challenges: (1) a lack of multi-level feature expression during consensus feature learning; (2) some nonlinear geometric structures in the data have not been fully utilized, leading to incomplete graph information representation; (3) the neglect of robust supervision from the original feature matrix in the multi-view clustering. To address these issues, we propose a Deep Multi-view Subspace Clustering via Hierarchical Diversity Optimization of Consensus Learning, termed as DMSC-HDOC. Our framework integrates three key modules: The hierarchical self-weighted fusion (HSF) module to resample the original features and learn more diverse features. On this basis, dual laplacian constraint (DLC) module are exploited to mine the geometric structure of the data samples. Finally, self-alignment contrast (SaC) is effectively used to supervise the consensus features of the original features. Extensive experiments on the several widely used datasets have shown the superiority of the proposed DMSC-HDOC compared to existing state-of-the-arts methods.