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Incomplete multi-view clustering (IMVC) is an unsupervised approach, amongwhich IMVC via contrastive learning has received attention due to its excellentperformance. The previous methods have the following problems: 1) Over-relianceon additional projection heads when solving the dimensional collapse problem inwhich latent features are only valid in lower-dimensional subspaces duringclustering. However, many parameters in the projection heads are unnecessary.2) The recovered view contain inconsistent private information and uselessprivate information will mislead the learning of common semantics due toconsistent learning and reconstruction learning on the same feature. To addressthe above issues, we propose a novel incomplete multi-view contrastiveclustering framework. This framework directly optimizes the latent featuresubspace, utilizes the learned feature vectors and their sub-vectors forreconstruction learning and consistency learning, thereby effectively avoidingdimensional collapse without relying on projection heads. Since reconstructionloss and contrastive loss are performed on different features, the adverseeffect of useless private information is reduced. For the incomplete data, themissing information is recovered by the cross-view prediction mechanism and theinconsistent information from different views is discarded by the minimumconditional entropy to further avoid the influence of private information.Extensive experimental results of the method on 5 public datasets show that themethod achieves state-of-the-art clustering results.

Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning