通过在线相互学习进行端到端的深度图表集群
IEEE transactions on neural networks and learning systems
|January 23, 2024
概括
本研究介绍了一种统一的深度图集群 (UDGC) 模型,该模型集成了深度嵌入和集群,用于增强图形神经网络优化. UDGC模型提供端到端集群,并减少图形数据分析中的计算复杂性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 图形理论 图形理论
背景情况:
- 深度图形模型通常使用单独的阶段进行神经网络优化和数据聚合.
- 现有的方法面临着局限性,因为聚类结果不能指导神经网络训练.
- 复杂的矩阵计算在聚合导致高计算负担.
研究的目的:
- 为端到端优化提出一个统一的深度图集群 (UDGC) 模型.
- 为了解决深度图形集群中的单独优化阶段的局限性.
- 为了减少深度图表集群任务中的计算复杂性.
主要方法:
- 开发了一个统一的深度图集群 (UDGC) 模型,利用在线相互学习.
- 在深层嵌入子空间中提取深度图表表示和节点拓知识.
- 采用局部保存损失用于嵌入聚合和集群分配生成.
- 训练了一个神经层以适应聚类结果,并优化了端到端的模型.
主要成果:
- UDGC模型实现了端到端的集群分配生成.
- 与传统方法相比,计算复杂性的显著降低.
- 通过广泛的实验证明了卓越的性能.
结论:
- 拟议的UDGC模型有效地统一了深层嵌入式提取和集群.
- 在线相互学习使深度图表集群的端到端优化成为可能.
- UDGC模型为图形集群任务提供了一种优越且计算效率高的方法.
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