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.

翻译：不完整多视图聚类（IMVC）旨在对仅部分可用的多视图数据进行聚类。这带来了两个主要挑战：有效利用多视图信息以及减轻视图缺失的影响。主流解决方案采用跨视图对比学习和缺失视图恢复技术。然而，它们要么因仅关注视图间共识而忽略了有价值的互补信息，要么因缺乏监督而提供了不可靠的恢复视图。为解决这些局限性，我们提出了一种新颖的面向不完整多视图聚类的统一鲁棒表示学习方法（URRL-IMVC）。URRL-IMVC通过整合来自多个视图和邻近样本的信息，直接学习一个对视图缺失条件具有鲁棒性的统一嵌入表示。首先，为克服跨视图对比学习的局限，URRL-IMVC引入了一个基于注意力的自编码器框架来融合多视图信息并生成统一嵌入。其次，URRL-IMVC通过KNN插补和数据增强技术，直接增强统一嵌入对视图缺失条件的鲁棒性，从而无需显式地进行缺失视图恢复。最后，引入了诸如聚类模块和编码器定制等增量改进以进一步提升整体性能。我们在多个基准数据集上对所提出的URRL-IMVC框架进行了广泛评估，证明了其具备最先进的性能。此外，我们进行了全面的消融研究以验证我们设计的有效性。