Multi-view Semantic Consistency based Information Bottleneck for Clustering
This work addresses multi-view clustering for unsupervised learning, but it appears incremental as it builds on existing methods by focusing on semantic consistency.
The paper tackles the problem of multi-view clustering by addressing the influence of private information and noise, introducing MSCIB which pursues semantic consistency to improve the information bottleneck, resulting in state-of-the-art performance on various datasets.
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.