3 papers
cs.LG2025
Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Training
Huaiyuan Xiao, Fadi Dornaika, Jingjun Bi
The advent of graph convolutional network (GCN)-based multi-view learning provides a powerful framework for integrating structural information from heterogeneous views, enabling ef…
cs.CV2025
MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network
Chenping Pei, Fadi Dornaika, Jingjun Bi
Existing Multi-view Clustering (MVC) methods based on subspace learning focus on consensus representation learning while neglecting the inherent topological structure of data. Desp…
cs.CV2025
A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data
Jingjun Bi, Fadi Dornaika
Recently, graph-based semi-supervised learning and pseudo-labeling have gained attention due to their effectiveness in reducing the need for extensive data annotations. Pseudo-labe…