Incomplete Multi-view Clustering via Prototype-based Imputation
This work addresses the problem of clustering data with missing views in multi-view learning, offering an incremental improvement for researchers in machine learning and data analysis.
The paper tackles incomplete multi-view clustering by proposing a dual-stream model that learns view-specific prototypes and sample-prototype relationships to preserve instance commonality and view versatility, achieving superior performance on six benchmarks compared to 11 existing approaches.
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.