多层次对比的多视图集群与双重自主监督学习
IEEE transactions on neural networks and learning systems
|April 11, 2025
概括
多级对比多视图集群 (MCMC) 通过使用最近的邻居作为正对和捕获多级结构来增强数据表示. 这种新的方法可以提高聚类的准确性和紧性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 多视图集群 (MVC) 集成多种数据视图以提高性能.
- 对比式学习在无监督表示学习中表现出色,但在MVC中存在局限性.
- 现有的对比的MVC方法忽略了最近的邻居和多层数据结构.
研究的目的:
- 提出一种新的端到端深度MVC方法,即多层次对比MVC (MCMC).
- 通过结合最近的邻居和多层结构来解决现有对比的MVC的局限性.
- 通过双重自我监督学习 (DSL) 提高多视图集群的紧性和准确性.
主要方法:
- 开发了MCMC,利用来自潜伏子空间的最近邻居作为实例级紧性的正对.
- 在集群,实例和原型上实施多层次对比学习 (MCL),以捕获数据结构.
- 使用DSL学习通过关联不同的结构表示来学习一致的集群分配.
主要成果:
- MCMC 显示了更好的集群内部紧密性和集群内部可分离性.
- 与现有方法相比,拟议的方法在集群性能方面取得了更高的准确性 (ACC).
- 这种方法有效地捕捉了与多视图数据集固有的多层次表示结构.
结论:
- 通过利用最近的邻居和多层次的对比学习,MCMC提供了多视图集群的显著进步.
- 集成的DSL确保在不同的代表级别一致和准确的集群分配.
- 拟议的方法为提高无监督多视图集群的性能提供了一个强大的框架.
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