Dornaika F, Bi J, Charafeddine J, Xiao H
University of the Basque Country, UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
University of the Basque Country, UPV/EHU, San Sebastian, Spain.
Neural Netw. 2025 May;185:107218. doi: 10.1016/j.neunet.2025.107218. Epub 2025 Feb 3.
Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed in semi-supervised learning, primarily on graph-structured data like citations and social networks. However, there exists a significant gap in applying these methods to non-graph multi-view data, such as collections of images. To bridge this gap, we introduce a novel deep semi-supervised multi-view classification model tailored specifically for non-graph data. This model independently reconstructs individual graphs using a powerful semi-supervised approach and subsequently merges them adaptively into a unified consensus graph. The consensus graph feeds into a unified GCN framework incorporating a label smoothing constraint. To assess the efficacy of the proposed model, experiments were conducted across seven multi-view image datasets. Results demonstrate that this model excels in both the graph generation and semi-supervised classification phases, consistently outperforming classical GCNs and other existing semi-supervised multi-view classification approaches. .
基于图的半监督学习方法在机器学习中越来越受欢迎，尤其是在处理标记数据成本高昂的情况时。图卷积网络（GCN）已广泛应用于半监督学习，主要用于处理诸如引用网络和社交网络等图结构数据。然而，将这些方法应用于非图多视图数据（如图像集合）时，存在很大差距。为了弥补这一差距，我们引入了一种专门为非图数据量身定制的新型深度半监督多视图分类模型。该模型使用强大的半监督方法独立重建各个图，随后将它们自适应地合并为一个统一的共识图。该共识图输入到一个包含标签平滑约束的统一GCN框架中。为了评估所提出模型的有效性，我们在七个多视图图像数据集上进行了实验。结果表明，该模型在图生成和半监督分类阶段均表现出色，始终优于经典GCN和其他现有的半监督多视图分类方法。
