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Multi-view clustering can make use of multi-source information forunsupervised clustering. Most existing methods focus on learning a fusedrepresentation matrix, while ignoring the influence of private information andnoise. To address this limitation, we introduce a novel Multi-view SemanticConsistency based Information Bottleneck for clustering (MSCIB). Specifically,MSCIB pursues semantic consistency to improve the learning process ofinformation bottleneck for different views. It conducts the alignment operationof multiple views in the semantic space and jointly achieves the valuableconsistent information of multi-view data. In this way, the learned semanticconsistency from multi-view data can improve the information bottleneck to moreexactly distinguish the consistent information and learn a unified featurerepresentation with more discriminative consistent information for clustering.Experiments on various types of multi-view datasets show that MSCIB achievesstate-of-the-art performance.

Multi-view Semantic Consistency based Information Bottleneck for Clustering