INCOMPLETE MULTI-VIEW CLUSTERING VIA REP- RESENTATION RECOVERY AND DIRECT SOFT- ASSIGNMENT ESTIMATION

Incomplete multi-view clustering (IMVC) aims to discover latent cluster struc- ture from multi-view data with partially observed views. Existing imputation- based methods commonly recover missing representations from observed views and then obtain cluster assignments from the completed representations. For a sample missing a target view, however, representation recovery and soft cluster- assignment estimation generally do not share the same reference samples and re- lation weights, so the latent representation and cluster-membership information already available in the target view are not used under a common sample relation. We propose ReSA, an IMVC method based on representation recovery and direct soft-assignment estimation. ReSA first learns cross-view relation representations for reference retrieval and relation-weight computation. Cross-view contrastive learning treats the relation representations of the same jointly observed instance as a positive pair and those of different instances as negative pairs; neighborhood distribution consistency further preserves local cross-view relations. For a sam- ple with a missing target view, ReSA selects the most strongly related references from samples observed in both the source and target views and computes their relation weights. The same references and weights are then reused to aggregate target-view latent representations for missing-representation recovery and target- view soft assignments for direct assignment estimation. Completed multi-view representations provide global clustering information, whereas observed or esti- mated view-specific assignments preserve view-specific cluster membership; both are integrated by global–view consensus clustering. Experiments on four IMVC benchmarks and multiple missing rates show that ReSA achieves leading cluster- ing performance. Comparisons with the trained model held fixed show that direct soft-assignment estimation is closer to the reference assignment computed from the complete target view under identical references and weights, and missing-view reconstruction confirms that the recovered representations retain decodable target- view content.