Multi-view Semantic Consistency based Information Bottleneck for Clustering

Abstract

Multi-view clustering leverages diverse information sources for unsupervised clustering. While existing methods primarily focus on learning a fused representation matrix, they often overlook the impact of private information and noise. To overcome this limitation, we propose a novel approach, the Multi-view Semantic Consistency based Information Bottleneck for Clustering (MSCIB). Our method emphasizes semantic consistency to enhance the information bottleneck learning process across different views. It aligns multiple views in the semantic space, capturing valuable consistent information from multi-view data. The learned semantic consistency improves the ability of the information bottleneck to precisely distinguish consistent information, resulting in a more discriminative and unified feature representation for clustering. Experimental results on diverse multi-view datasets demonstrate that MSCIB achieves state-of-the-art performance. In comparison with the average performance of the other contrast algorithms, our approach exhibits a notable improvement of at least 4%.