Optimal Tensor Bipartite Graph Learning

Abstract

are concerned in this paper with a multi-view clustering framework based on bipartite graphs. And we propose an efficient multiview clustering method, Optimal Tensor Bipartite Graph Learning for multi-view clustering (OTBGL). Our model is a novel tensorized bipartite graph based multi-view clustering method with low tensorrank constraint. Firstly, to remarkably reduce the computational complexity, we leverage the bipartite graphs of different views instead of full similarity graphs of the corresponding views. Secondly, we measure the similarity between bipartite graphs of different views by minimizing the tensor Schatten p-norm as a tighter tensor rank approximation and explore the spatial low-rank structure embedded in intra-view graphs by minimizing the l1,2-norm of learned graphs. Thirdly, we provide an efficient algorithm suitable for processing large-scale data. Extensive experimental results on six benchmark datasets indicate our proposed OTBGL is superior to the state-of-the-art methods.