An effective bipartite graph fusion and contrastive label correlation for multi-view multi-label classification

Abstract

Graph-based multi-view multi-label learning effectively utilizes the graph structure underlying the samples to integrate information from different views. However, most existing graph construction techniques are computationally complex. We propose an anchor-based bipartite graph fusion method to accelerate graph learning and perform label propagation. First, we employ an ensemble learning strategy that assigns weights to different views to capture complementary information. Second, heterogeneous graphs from different views are linearly fused to obtain a consensus graph, and graph comparative learning is utilized to bring inter-class relationships closer and enhance the quality of label correlation. Finally, we incorporate anchor samples into the decision-making process and jointly optimize the model using bipartite graph fusion and soft label classification with nonlinear extensions. Experimental results on multiple real-world benchmark datasets demonstrate the effectiveness and scalability of our approach compared to state-of-the-art methods.