Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion

Abstract

Multi-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of learning an improved graph for each view,which has achieved good results,but the time and space complexity are high,which limits its application on large-scale datasets.This paper proposes a multi-view clustering method based on bipartite graph diffusion across views to address problems with high time and space complexity.It reduces the complexity to linear complexity,making it suitable for large-scale clustering tasks.The specific method involves using a bipartite graph instead of a complete graph for cross-view graph diffusion and modifying the cross-view graph diffusion formula based on the complete graph to accommodate the bipartite graph input.Experimental results on six benchmark datasets demonstrate that the proposed method outperforms most existing multi-view clustering methods in terms of clustering accuracy and computational efficiency.In small-scale datasets,accuracy and other metrics are generally more than 5% higher than those of comparison algorithms.In large-scale datasets,the advantage is even more pronounced,with indicators such as ACC and NMI are 15%~30% higher than the comparison algorithms.