In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster instances should share a common pattern, and ii) view versatility refers to that cross-view samples should own view-specific patterns. To this end, we design a novel dual-stream model which employs a dual attention layer and a dual contrastive learning loss to learn view-specific prototypes and model the sample-prototype relationship. When the view is missed, our model performs data recovery using the prototypes in the missing view and the sample-prototype relationship inherited from the observed view. Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC. Extensive experiments demonstrate the superiority of our method on six challenging benchmarks compared with 11 approaches. The code will be released.

翻译：本文研究如何实现不完整多视角聚类（IMvC）所期望的两个特性。即：i）实例共性，指聚类内的实例应共享共同模式；ii）视角多样性，指跨视角样本应拥有视角特有的模式。为此，我们设计了一种新颖的双流模型，该模型采用双重注意力层和双重对比学习损失来学习视角特定原型并建模样本-原型关系。当某一视角缺失时，我们的模型利用缺失视角中的原型以及从观测视角继承的样本-原型关系进行数据恢复。得益于双流模型的设计，聚类特定信息和视角特定信息都能被捕获，从而能够保留实例共性和视角多样性以促进不完整多视角聚类。大量实验表明，与11种方法相比，我们的方法在六个具有挑战性的基准数据集上具有优越性。代码将开源。