  Incomplete multi-view clustering (IMVC) aims to cluster multi-view data that
are only partially available. This poses two main challenges: effectively
leveraging multi-view information and mitigating the impact of missing views.
Prevailing solutions employ cross-view contrastive learning and missing view
recovery techniques. However, they either neglect valuable complementary
information by focusing only on consensus between views or provide unreliable
recovered views due to the absence of supervision. To address these
limitations, we propose a novel Unified and Robust Representation Learning for
Incomplete Multi-View Clustering (URRL-IMVC). URRL-IMVC directly learns a
unified embedding that is robust to view missing conditions by integrating
information from multiple views and neighboring samples. Firstly, to overcome
the limitations of cross-view contrastive learning, URRL-IMVC incorporates an
attention-based auto-encoder framework to fuse multi-view information and
generate unified embeddings. Secondly, URRL-IMVC directly enhances the
robustness of the unified embedding against view-missing conditions through KNN
imputation and data augmentation techniques, eliminating the need for explicit
missing view recovery. Finally, incremental improvements are introduced to
further enhance the overall performance, such as the Clustering Module and the
customization of the Encoder. We extensively evaluate the proposed URRL-IMVC
framework on various benchmark datasets, demonstrating its state-of-the-art
performance. Furthermore, comprehensive ablation studies are performed to
validate the effectiveness of our design.
