UNAGI: 统一的邻居意识图神经网络用于多视图集群
Zheming Xu1, Congyan Lang1, Lili Wei1
1Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
概括
本研究介绍了UNAGI,这是一种用于多视图聚类的新方法,它统一了图形结构学习和表示学习. 通过解决现有的基于多视图精制的集群方法的局限性,UNAGI提高了集群性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 图形神经网络的神经网络
背景情况:
- 多视图精制基集群 (MGRC) 方法使用图形神经网络 (GNN) 来学习集群的数据拓.
- 目前的MGRC方法采用了一个分离的两阶段过程,忽视了交叉视图的一致性和语义信息.
研究的目的:
- 为多视图集群 (UNAGI) 提出一个统一的邻居意识图形神经网络.
- 通过整合图形拓优化和样本表示学习来解决现有的MGRC方法的局限性.
主要方法:
- 开发了一个新的框架,通过可微分图形适配器将图形拓优化和样本表示结合起来,用于统一的训练.
- 引入了用于强大的图形学习和采访视图形态拓对齐的规范化技术,使用邻居意识的伪标签.
主要成果:
- 在七个不同的数据集中,UNAGI表现出卓越的集群性能.
- 统一的培训范式和新的规范化显著提高了聚类准确性.
结论:
- 通过克服以前MGRC方法的局限性,UNAGI为多视图集群提供了有效的解决方案.
- 拟议的方法展示了综合图形学习和复杂数据的表示学习的好处.
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