Efficient Co-Clustering via Bipartite Graph Factorization

Sample-anchor co-clustering has demonstrated potential in improving clustering efficiency; however, existing methods face two major limitations. First, the intrinsic geometric relationships among anchors are often overlooked, leading to insufficient smoothness in the anchor cluster structure. Second, the inability to directly infer discrete one-hot pseudo-labels for both samples and anchors undermines the stability and interpretability of clustering results. To address these challenges, we propose BGFC, a bipartite graph factorization clustering model. BGFC employs non-negative matrix factorization of the bipartite graph to directly generate one-hot pseudo-labels for both samples and anchors, enhancing local consistency in label assignments. In addition, a compact anchor similarity graph is constructed and refined via low-rank decomposition to explicitly promote the consistency of pseudo-labels among geometrically related anchors. An alternating optimization algorithm is developed to jointly update all model variables, enabling efficient and scalable training. Extensive experiments on benchmark datasets demonstrate that BGFC consistently outperforms state-of-the-art co-clustering methods in both clustering performance and computational efficiency.