Learning Observation Robust Representations for Unbalanced Incomplete Multi-View Clustering

Multi-view clustering is commonly studied under the assumption that every sample is observed in all views. In practice, however, some views are missing, and the amount of observed data can vary substantially across views, producing strong views with dense observations and weak views with sparse ones. Existing incomplete multi-view clustering methods mainly recover missing information through completion or alignment, while unbalanced methods adjust the contributions of strong and weak views. These strategies do not directly constrain the consensus representation of the same sample when its observed-view set changes. As a result, the representation may vary across different missing patterns and may still correlate with frequently occurring observation patterns. We propose COIN, a content–observation invariance framework for unbalanced incomplete multi-view clustering. COIN uses an observation factor to encode the missing pattern, while the content factor for clustering is computed from mask-gated view features. Nested content invariance encourages the same sample to maintain a consistent representation under coarser missing patterns, and rebalanced decorrelation reduces linear correlation between the content factor and the missing pattern after rare patterns are upweighted. Experiments on five unbalanced incomplete multi-view clustering benchmarks show that COIN consistently outperforms representative incomplete and unbalanced methods.