Dual-Constraint Multi-view Fuzzy Clustering with Scalable Anchor Graph Learning

Anchor graph has gained considerable attention in the field of multi-view clustering due to its capability to handle large-scale datasets and superior clustering performance. However, current approaches hypothesize that views select the equal number of anchors and only share the anchor graph of single magnitude. This limits their representability and ignores the varying data distributions from multiple views. In addition, most of anchor strategies attain the clustering labels by performing post-processing on anchor graph, which consumes additional time and exists the loss of information. So as to address the above problems, this paper presents a Dual-Constraint Multi-view Fuzzy Clustering with Scalable Anchor Graph Learning (DMFC-SAGL) that explores the anchor graphs of different magnitudes and directly derives the clustering labels via fuzzy clustering. In particular, DMFC-SAGL first learns the multi-scale anchor graphs based different quantity of anchors to accommodate the distinct data distributions and extract more comprehensive information. Then, by carrying out fuzzy clustering for anchor graphs, the soft labels can be generated directly without additional post-processing. Moreover, DMFC-SAGL imposes the dual-constraint of low-rank tensor and orthogonality during label learning to ensure the information of consistency and diversity among multi-scale anchor graphs. Experiments with advanced baselines on the seven multi-view datasets indicate the superiority of the proposed method. The code is available at https://github.com/whbdmu/DMFC-SAGL.