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

• URL: http://arxiv.org/abs/2008.10208v2
• Date: Sat, 3 Jul 2021 10:02:51 GMT
• Title: Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
• Authors: Youwei Liang, Dong Huang, Chang-Dong Wang, and Philip S. Yu
• Abstract summary: Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. We propose a new multi-view graph learning framework, which for the first time simultaneously models multi-view consistency and multi-view inconsistency in a unified objective function. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.
• Score: 65.76554214664101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency across multiple views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm which is able to obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.

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