基于原型的对比图集群网络,用于减少虚假负数
Cuihua Ma1,2,3, Chaosheng Tang4,5, Ziqi Deng2
1School of Information and Communication Engineering, Hainan University, Haikou, 570228, Hainan, China.
这项研究引入了一种新的原型驱动的对比图形集群方法,以提高图形集群的准确性. 它有效地避免了自我监督图形对比学习 (SS-GCL) 中的错误负值,以提高性能.
科学领域:
- 图表 机器学习 机器学习
- 没有监督的学习学习.
- 数据挖掘 数据挖掘
背景情况:
- 对比图集群方法通过使用多视图增强和对比损失来提高性能.
- 自主监督图形对比学习 (SS-GCL) 减少了对标记数据的依赖,但由于伪标记,通常会出现错误负值.
研究的目的:
- 解决现有的SS-GCL方法在图形集群中的局限性.
- 提出一种新的原型驱动的对比图集群网络,可以减轻虚假负数并提高集群效率.
主要方法:
- 一个以原型驱动的网络,使用数据驱动的集群中心 (原型) 来形成高可靠性样本集和聚合增强嵌入式.
- 一个交叉视图解的对比学习机制,仅在阳性样本上使用平均平方误差对比损失函数.
- 在视图之间对齐增强的积极样本嵌入,以防止虚假负面生成.
主要成果:
- 拟议的方法有效地防止产生假负样本.
- 实验结果显示,与最先进的基线方法相比,性能优越.
- 在多个数据集的准确性和集群有效性方面取得了明显的改进.
结论:
- 由原型驱动的对比图集群网络为SS-GCL中的假负问题提供了强大的解决方案.
- 该方法实现了最先进的性能,突出了其用于高级图形集群任务的潜力.
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