异质图形卷积网络用于多视图半监督分类
Shiping Wang1, Sujia Huang1, Zhihao Wu1
1College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
概括
本研究介绍了一种新的异质图形卷积网络 (HGCN),用于多视图表示学习. 该方法有效地捕捉跨异质数据视图的语义相互作用,以改进半监督分类.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 数据挖掘 数据挖掘
背景情况:
- 现有的多视图学习方法经常独立处理数据视图,忽略了视图间的语义交互.
- 最近的图形卷积网络 (GCN) 方法汇总了视图特定的表示,未能在异质数据中建模复杂的关系.
研究的目的:
- 提出一个统一的框架,用于从多视图数据集学习语义表示,将其视为异质图.
- 解决现有方法在捕获交叉视图语义依赖性的局限性.
主要方法:
- 一种新的方法将多视图数据建模为具有共享节点和视图特定边缘类型的异质图.
- 使用异质图卷积网络 (HGCN) 来提取语义表示.
- 采用一个自适应学习的图形拉普拉斯矩阵从多种类型的边缘.
主要成果:
- 拟议的HGCN-MVSC方法在半监督分类任务中表现出卓越的性能.
- 与最先进的竞争对手相比,在八个公共数据集中取得了令人鼓舞的结果.
结论:
- 将多视图数据表示为异质图是表示学习的一个有希望的方向.
- 高基CN-MVSC框架有效地捕捉复杂的语义相互作用,以加强半监督分类.
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