A missing-pattern tree groups data by missing patterns for per-group clustering, followed by uncertainty-weighted ensemble and knowledge distillation to better exploit available pairs in incomplete multi-view clustering.
Incomplete multi-view cluster- ing via prototype-based imputation
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abstract
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.
representative citing papers
Straight-path flow matching between paired latent representations outperforms diffusion-based methods for incomplete multi-view clustering by preserving cluster structure during view completion.
citing papers explorer
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Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering
A missing-pattern tree groups data by missing patterns for per-group clustering, followed by uncertainty-weighted ensemble and knowledge distillation to better exploit available pairs in incomplete multi-view clustering.
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Straight-Path Flow Matching for Incomplete Multi-View Clustering
Straight-path flow matching between paired latent representations outperforms diffusion-based methods for incomplete multi-view clustering by preserving cluster structure during view completion.