图学习与双噪声去除多视图集群的多视图集群.
Zhe Chen1, Mingzhi Zhu1, Hui Li2
1School of Computer Science and Technology, Anhui University of Technology, Ma'anshan 243032, China.
概括
本研究介绍了用于多视图集群的双重噪声删除 (AGLDR) 的图学习. AGLDR有效地消除了不同视图的噪声,提高了集群精度和稳定性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 现有的基于的多视图图表集群方法在视图上的噪声消除方面存在困难.
- 这种噪音导致不准确的共识表示和降低集群质量.
研究的目的:
- 提出一种新的方法,即用双噪声去除 (AGLDR) 来进行多视图集群的Anchor Graph Learning.
- 为了同时学习一个一致的图,并消除视图特定的噪音.
主要方法:
- 在一致的图上引入了一个低级约束,以捕捉全球相关性.
- 最小化F-规范和L2,1规范的噪声术语,分别消除高斯和拉普拉斯噪声.
- 开发了一种新的算法,AGLDR,用于在多视图集群中有效降低噪音.
主要成果:
- 与最先进的方法相比,AGLDR表现出卓越的性能.
- 拟议的方法实现了更高的集群精度.
- 在多视图聚类任务中,AGLDR表现出更强大的稳定性.
结论:
- AGLDR有效地解决了针对多视图集群的降噪的关键差距.
- 该算法为集群复杂的多视图数据提供了强大而准确的解决方案.
- 实验结果证实了AGLDR对现有技术的优越性.
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