Metric learning-enhanced semi-supervised Graph Convolutional Network for multi-view learning

Abstract

Multi-view learning utilizes data from diverse perspectives or modalities, integrating complementary information from various sources. It plays a crucial role in intelligent systems and finds extensive applications in fields such as computer vision, recommender systems, and natural language processing. With the increasing complexity and heterogeneity of real-world data, the integration of Graph Convolutional Networks (GCNs) in multi-view learning scenarios is becoming increasingly important. Despite the advances in GCNs, it remains a major challenge to effectively generalize models and improve their stability across different data views. In this paper, we present a novel framework, the Enhanced Triplet Loss Based Semi-Supervised Graph Convolutional Network for Multi-View Learning (MV-TriGCN), which addresses these challenges through three primary innovations. First, we propose an enhanced triplet loss for deep metric learning tailored to the hidden features of GCNs based on semi-hard negative sample selection. Second, view graphs are constructed using the classical KNN scheme and a semi-supervised flexible method to improve the diversity of data structure representation resulting in a more stable hypothesis space. Moreover, we learn an end-to-end multi-view GCN by merging all available graphs and utilizing the aggregated cross-entropy losses and deep metric losses. Finally, we introduce a stepwise training strategy that allows the model to adapt to the losses during different optimization phases. Extensive experiments show that our method outperforms existing state-of-the-art approaches in terms of accuracy and stability.¹