Incomplete Multi-View Clustering via Mutual Information

Incomplete multi-view clustering focus on mining useful information from low-quality multiple sources, such as missing and distorted data that are prevalent in real life. However, after representation learning and the processing of incomplete information, existing methods often leave representations containing information task-irrelevant information. In addition, the separation between missing data imputation and clustering tasks leads to sub-optimal multi-view clustering performance. To address these issues, we propose an incomplete multi-view clustering method based on mutual information. For the problem of task-irrelevant information, we use incomplete view prediction to extract sufficient and minimal task-relevant information and provide theoretical proof from the perspective of mutual information. For the problem of separation between missing data imputation and clustering tasks, we integrate incomplete-view prediction with contrastive clustering, collaboratively enhancing the clustering performance. Comparative experiments on five public datasets, under both complete and incomplete scenarios, reveal that our method outperforms nine other competing approaches, demonstrating its effectiveness and robustness in handling multi-view data.