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Group Correspondence: A Statistical Perspective for Incomplete Multi-View Clustering Augmentation

Cross-view consistency is the fundamental property of multiview clustering. However, in incomplete multi-view scenarios, existing methods can only pursue consistency through the paired data while ignoring the information in unpaired data. In this paper, we show a new insight from the data pattern and provide a novel perspective to incorporate unpaired data for consistency maximization by mining group correspondence. We first formulate cross-view consistency in a statistical perspective to by-pass the strict demand of instance correspondence, and then propose a technique to construct corresponding groups across views to enhance the objective of consistency maximization. Our proposal can be used as a universal plug-in to augment existing approaches. We test the efficacy and generality of our proposal by adapting it to two base methods as augmentations and comparing the augmented models against the original ones and other baselines. Experiment results demonstrate the effectiveness of our proposal and validate the value of our insight.