  Multi-view clustering can make use of multi-source information for
unsupervised clustering. Most existing methods focus on learning a fused
representation matrix, while ignoring the influence of private information and
noise. To address this limitation, we introduce a novel Multi-view Semantic
Consistency based Information Bottleneck for clustering (MSCIB). Specifically,
MSCIB pursues semantic consistency to improve the learning process of
information bottleneck for different views. It conducts the alignment operation
of multiple views in the semantic space and jointly achieves the valuable
consistent information of multi-view data. In this way, the learned semantic
consistency from multi-view data can improve the information bottleneck to more
exactly distinguish the consistent information and learn a unified feature
representation with more discriminative consistent information for clustering.
Experiments on various types of multi-view datasets show that MSCIB achieves
state-of-the-art performance.
