Pseudo-Supervision Affinity Propagation for Efficient and Scalable Multiview Clustering

Anchor graph-based multiview clustering (AGMVC) demonstrates high efficiency and satisfactory performance. However, it still suffers from limitations such as single-structure similarity measurement, high time expenditure for large-scale anchor graph partitioning, and limited generalization ability. To alleviate the instability problem of single-structure information, this article proposes an anchor graph construction method that learns local and global (LG) structures simultaneously. To eliminate the need for graph partitioning and address the out-of-sample problem, we develop a landmark learning method to produce structural anchors, and further propose a pseudo-supervision affinity propagation (PSAP) framework. This framework jointly optimizes graph construction and landmark learning to disentangle the in-cluster distribution between samples and anchors while accelerating convergence. In addition, our framework introduces a clustering inference partition (CIP) strategy to directly output clustering results without the need for time-consuming postprocessing. Extensive experiments validate the efficiency and effectiveness of our framework. Our code is publicly available at https://github.com/W-Xinxin/PSAP.