无监督图形异常检测频率自适应图形神经网络
Ming Gu1, Gaoming Yang2, Zhuonan Zheng1
1College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.
概括
本研究介绍了用于无监督图形异常检测 (FAGAD) 的频率自适应图形神经网络. 通过适应性地将信号跨频率融合,FAGAD有效地识别图形异常,在没有标记数据的情况下获得最先进的结果.
科学领域:
- 图形神经网络 图形神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 无监督图形异常检测 (UGAD) 方法通常依赖图形神经网络 (GNN) 过低频图形信号.
- 异常可以将图形信号频率转移到更高的频段,违反GNN假设并阻碍检测.
- 现有的先进的图形过器通常需要异常标签,从而限制了它们在现实世界中的适用性.
研究的目的:
- 解决目前无监督图形异常检测方法的局限性.
- 提出一种新的方法,以无监督的方式设计有效的图形过器.
- 开发一个图形神经网络,能够处理由异常引起的频率转移.
主要方法:
- 提出频率自我适应图形神经网络 (FAGAD).
- 通过使用全通信号作为参考,在多个频段中自适应地融合图形信号.
- 通过自我监督的学习方法优化模型,用于表示生成.
主要成果:
- 在异常检测任务上,FAGAD展示了最先进的性能.
- 该方法在人工生成的和真实世界的数据集上都实现了高精度.
- 拟议的方法有效地处理了图形信号频率转移所带来的挑战.
结论:
- FAGAD提供了一个强大的解决方案,用于无监督的图形异常检测.
- 自主监督学习框架使有效的表现学习能够在没有标记数据的情况下实现.
- 多频信号的自适应融合是FAGAD卓越性能的关键.
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