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：在本文中,我们研究如何通过不完全的多视图群集(IMVC)实现两个高度预期的特征。 即,一)实例共性是指集组内情况应当有一个共同模式,二)认为交叉视图样本应当具有不同的视角模式。为此,我们设计了一个新的双流模式,采用双关注层和双对比学习损失模式,学习特定视图原型和样样-样型关系模式。当这种观点被忽略时,我们的模型使用缺失视图中的原型和从观察到的视图中继承的样本-样样关系进行数据恢复。由于我们的双流模式,可以捕捉集和特定视图的信息,因此,可以保留实例共性和多视角,以促进IMVC。广泛的实验表明我们的方法优于六项具有挑战性的基准,而有11种方法。代码将被发布。