Structure-Enhanced Self-Supervised Weighted Information Bottleneck for Multiview Clustering

This paper presents S2WIB, a new method for multiview clustering (organizing data points into groups when information comes from multiple different sources or perspectives). The method improves on existing approaches by using both view quality and self-supervised learning (learning from patterns the model finds in data itself) to determine how much weight to give each data source, and by considering both complementary information and consistency between individual view clustering results and the final result.