Attention-based deep incomplete multi-view clustering via bi-alignment guidance

Abstract

Abstract Deep learning-based incomplete multi-view clustering has gained prominence for clustering tasks due to its superior feature learning capabilities across multiple views. Nevertheless, considering that the data incompleteness significantly weakens the adequate information of multi-view data, existing methodologies often prioritize filling missing data with the average values of observed cross-view features. Yet, they do not consider using the intrinsic structural relationship among diverse views within the same cluster. These approaches may inadvertently introduce noise or irrelevant information, degrading clustering performance when using recovered data. To address these limitations, we propose the Attention-based Deep Incomplete Multi-view Clustering via Bi-Alignment Guidance (ADIMC-BAG). Specifically, we design an attention layer module to enhance the compactness of view-specific sample-prototype relationships and recover the missing data. Besides, we develop a bi-alignment guidance strategy that ensures the learning of prototypes consistency across views and further enables securing more discriminative features and precise cluster assignments. By capturing the compactness of the view-specific sample-prototype relationship and the consistency of cross-view prototypes, ADIMC-BAG can acquire the commonality of within-cluster samples across views, which is conducive to restoring missing data. Experiments on seven multi-view benchmarks demonstrate the method’s effectiveness, advancing incomplete multi-view clustering research and providing a robust solution for real-world scenarios.