  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.
