Reliability-aware dual graph convolutional network for multi-view learning

Chen, Hongrong

doi:10.1016/j.knosys.2026.116439

Reliability-aware dual graph convolutional network for multi-view learning

2026-06-01

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DOI：10.1016/j.knosys.2026.116439

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With the rapid development of multimedia technologies and the explosive growth of multi-source heterogeneous data, multi-view learning has attracted increasing attention. Although existing methods have effectively modeled intra-view and inter-view relationships, inevitable noise in real-world scenarios still leads to imbalanced view quality, which in turn introduces conflicts during view integration and causes decision bias in the fused representations. To address these challenges, we propose a reliability-aware dual graph convolutional network to enhance the reliability of view fusion. Specifically, we introduce a reliability-guided Bayesian inference mechanism to characterize view quality and learn shared representations across views under the guidance of view evidence. Moreover, to comprehensively capture both intra-view and inter-view data dependencies, we propose a dual graph convolutional network that collaboratively models intra-view complementarity and inter-view consistency, thereby improving robustness and overall learning stability. Experiments on multiple benchmark datasets demonstrate the effectiveness and robustness of the proposed method for multi-view semi-supervised classification.

Keywords:

Multi-view learning

Graph convolutional network

Deep generative model

Semi-supervised learning

Reliability-aware dual graph convolutional network for multi-view learning

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