Chen Yuhong, Wu Zhihao, Chen Zhaoliang, Dong Mianxiong, Wang Shiping
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China.
Department of Sciences and Informatics, Muroran Institute of Technology, Muroran 050-8585, Japan.
Neural Netw. 2023 Nov;168:161-170. doi: 10.1016/j.neunet.2023.09.006. Epub 2023 Sep 12.
Graph convolutional network has been extensively employed in semi-supervised classification tasks. Although some studies have attempted to leverage graph convolutional networks to explore multi-view data, they mostly consider the fusion of feature and topology individually, leading to the underutilization of the consistency and complementarity of multi-view data. In this paper, we propose an end-to-end joint fusion framework that aims to simultaneously conduct a consistent feature integration and an adaptive topology adjustment. Specifically, to capture the feature consistency, we construct a deep matrix decomposition module, which maps data from different views onto a feature space obtaining a consistent feature representation. Moreover, we design a more flexible graph convolution that allows to adaptively learn a more robust topology. A dynamic topology can greatly reduce the influence of unreliable information, which acquires a more adaptive representation. As a result, our method jointly designs an effective feature fusion module and a topology adjustment module, and lets these two modules mutually enhance each other. It takes full advantage of the consistency and complementarity to better capture the more intrinsic information. The experimental results indicate that our method surpasses state-of-the-art semi-supervised classification methods.
图卷积网络已被广泛应用于半监督分类任务。尽管一些研究试图利用图卷积网络来探索多视图数据，但它们大多分别考虑特征和拓扑结构的融合，导致多视图数据的一致性和互补性未得到充分利用。在本文中，我们提出了一个端到端的联合融合框架，旨在同时进行一致的特征整合和自适应的拓扑调整。具体来说，为了捕捉特征一致性，我们构建了一个深度矩阵分解模块，该模块将来自不同视图的数据映射到一个特征空间，以获得一致的特征表示。此外，我们设计了一种更灵活的图卷积，允许自适应地学习更稳健的拓扑结构。动态拓扑结构可以大大减少不可靠信息的影响，从而获得更具适应性的表示。因此，我们的方法联合设计了一个有效的特征融合模块和一个拓扑调整模块，并使这两个模块相互增强。它充分利用了一致性和互补性，以更好地捕捉更内在的信息。实验结果表明，我们的方法优于现有的半监督分类方法。
