用高阶对比学习进行图形集群
Wang Li1, En Zhu1, Siwei Wang1
1School of Computer Science, National University of Defense Technology, Changsha 410000, China.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
使用高阶对比学习 (GCHCL) 的图形集群通过解决手动增强和特征级限制来改善无监督的图形集群. 这种方法通过将结构信息纳入更强大的嵌入来提高性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 图形集群是一个关键的无监督学习任务.
- 对比式学习已经推进了图形集群,但也面临着挑战.
- 现有的方法受到手动增强的影响,导致语义漂移和特征级焦点忽视图形结构.
研究的目的:
- 提出一种新的方法,即高阶对比学习 (GCHCL) 的图形集群,以克服当前图形集群技术的局限性.
- 通过整合更高阶结构信息和自动视图生成来增强无监督图形集群.
主要方法:
- GCHCL使用拉普拉斯平滑与多种规范化构建了两个视图,并使用结构对齐损失.
- 它从基于结构的相似性构建了一个对比的相似性矩阵,将其与一个身份矩阵对齐,以增强社区学习.
- 该方法直接学习集群友好的嵌入,消除了对单独集群模块的需求,并实现了可扩展性.
主要成果:
- 在五个数据集中,GCHCL表现出显著的有效性.
- 该模型在小型和中型数据集上比较强的基线平均提高了3%的准确性.
- 在最大的数据集上,GCHCL获得了81.92%的准确性,克服了其他方法所面临的内存缺失问题.
结论:
- 通过利用高阶结构信息,GCHCL为图形集群提供了强大且可扩展的解决方案.
- 拟议的方法有效地解决语义漂移问题,并通过整合结构层次的对比学习来提高性能.
- GCHCL提供了卓越的准确性和效率,特别是在大规模的图形集群任务中.
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