Towards Adaptive Consensus Graph: Multi-View Clustering via Graph Collaboration

Abstract

Multi-view clustering is a long-standing important task, however, it remains challenging to exploit valuable information from the complex multi-view data located in diverse highdimensional spaces. The core issue is the effective collaboration of multiple views to holistically uncover the essential correlations between multi-view data through graph learning. Furthermore, it is indispensable for most existing methods to introduce an additional clustering step to produce the final clusters, which evidently reduces the uniform relationship between graph learning and clustering. Based on the above considerations, in this paper, we present a novel method named multi-view clustering via graph collaboration (MCGC). Based on the low-dimensional representation space developed by MCGC, it first perceives the correlations between samples in each individual view under the supervision of the Hilbert-Schmidt independence criterion (HSIC). Then, MCGC proposes learning a consensus graph by adaptively collaborating between all the views, which is able to uncover the essential structure of the multi-view data. Meanwhile, by imposing the rank constraint on the Laplacian matrix of the consensus graph to partition the multi-view data naturally into the required number of clusters, the optimal clustering results can be obtained directly without any postprocessing steps. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on 5 benchmark multi-view datasets demonstrate that MCGC markedly outperforms the state-of-the-art baselines.