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Graph learning has emerged as a promising technique for multi-view clusteringwith its ability to learn a unified and robust graph from multiple views.However, existing graph learning methods mostly focus on the multi-viewconsistency issue, yet often neglect the inconsistency across multiple views,which makes them vulnerable to possibly low-quality or noisy datasets. Toovercome this limitation, we propose a new multi-view graph learning framework,which for the first time simultaneously and explicitly models multi-viewconsistency and multi-view inconsistency in a unified objective function,through which the consistent and inconsistent parts of each single-view graphas well as the unified graph that fuses the consistent parts can be iterativelylearned. Though optimizing the objective function is NP-hard, we design ahighly efficient optimization algorithm which is able to obtain an approximatesolution with linear time complexity in the number of edges in the unifiedgraph. Furthermore, our multi-view graph learning approach can be applied toboth similarity graphs and dissimilarity graphs, which lead to two graphfusion-based variants in our framework. Experiments on twelve multi-viewdatasets have demonstrated the robustness and efficiency of the proposedapproach.

Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency