Incomplete multi-view clustering is a challenging and non-trivial task to provide effective data analysis for large amounts of unlabeled data in the real world. All incomplete multi-view clustering methods need to address the problem of how to reduce the impact of missing views. To address this issue, we propose diffusion completion to recover the missing views integrated into an incomplete multi-view clustering framework. Based on the observable views information, the diffusion model is used to recover the missing views, and then the consistency information of the multi-view data is learned by contrastive learning to improve the performance of multi-view clustering. To the best of our knowledge, this may be the first work to incorporate diffusion models into an incomplete multi-view clustering framework. Experimental results show that the proposed method performs well in recovering the missing views while achieving superior clustering performance compared to state-of-the-art methods.

翻译：不完全多视图聚类是一项具有挑战性且非平凡的任务，旨在为现实世界中大量未标注数据提供有效的数据分析。所有不完全多视图聚类方法都需要解决如何减少缺失视图影响的问题。针对这一问题，我们提出扩散补全方法，将缺失视图的恢复整合到不完全多视图聚类框架中。基于可观测视图信息，使用扩散模型恢复缺失视图，然后通过对比学习学习多视图数据的一致性信息，以提升多视图聚类的性能。据我们所知，这可能是首个将扩散模型融入不完全多视图聚类框架的工作。实验结果表明，所提方法在恢复缺失视图方面表现良好，同时相比现有最先进方法实现了更优的聚类性能。