Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
This addresses the challenge of incomplete multi-view data for semi-supervised learning applications, but it is incremental as it builds on existing graph fusion and imputation techniques.
The paper tackles the problem of view missing in graph-based multi-view semi-supervised learning, which can distort local structures and degrade classification, by proposing AGF-TI, a method that uses adversarial graph fusion and tensorial imputation to achieve superior performance compared to state-of-the-art methods, as validated by extensive experiments on various datasets.
View missing remains a significant challenge in graph-based multi-view semi-supervised learning, hindering their real-world applications. To address this issue, traditional methods introduce a missing indicator matrix and focus on mining partial structure among existing samples in each view for label propagation (LP). However, we argue that these disregarded missing samples sometimes induce discontinuous local structures, i.e., sub-clusters, breaking the fundamental smoothness assumption in LP. Consequently, such a Sub-Cluster Problem (SCP) would distort graph fusion and degrade classification performance. To alleviate SCP, we propose a novel incomplete multi-view semi-supervised learning method, termed AGF-TI. Firstly, we design an adversarial graph fusion scheme to learn a robust consensus graph against the distorted local structure through a min-max framework. By stacking all similarity matrices into a tensor, we further recover the incomplete structure from the high-order consistency information based on the low-rank tensor learning. Additionally, the anchor-based strategy is incorporated to reduce the computational complexity. An efficient alternative optimization algorithm combining a reduced gradient descent method is developed to solve the formulated objective, with theoretical convergence. Extensive experimental results on various datasets validate the superiority of our proposed AGF-TI as compared to state-of-the-art methods. Code is available at https://github.com/ZhangqiJiang07/AGF_TI.