大规模的多视图集群通过表示和多图对齐的联合学习来实现
IEEE transactions on neural networks and learning systems
|October 14, 2025
概括
这项研究介绍了ARMGA,这是一种用于大规模多视图聚类的新方法. ARMGA通过共同学习表示和对齐多个图表来提高聚类性能,从而提高不同数据视图的一致性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 基于的聚类是大规模数据的关键技术.
- 多视图数据在平衡个人图的独特性与整体一致性方面存在挑战.
研究的目的:
- 提出一个大规模的多视图集群 (MVC) 方法,ARMGA,共同学习表示和多图对齐.
- 为了应对在多视图基聚类中平衡独特性和一致性的挑战.
主要方法:
- ARMGA使用一个统一的框架来同时学习单视图表示和基于虚拟图的多图对齐.
- 它在适应性表示的张量上使用了Schatten-p规范,以加强交叉视图的一致性.
- 来自低等级表示的等边角信息被用于减轻噪声和减少计算复杂性.
主要成果:
- 在9个数据集上,ARMGA在聚类性能上显著改善,比其他算法增加了2%-10%.
- 与现有方法相比,该方法保持了较低的时间复杂性.
- ARMGA有效地利用跨观点的互补信息来增强整体结构和共识.
结论:
- 通过整合表示学习和多图对齐,ARMGA为大规模多视图聚类提供了有效的解决方案.
- 拟议的方法提高了交叉视图的一致性和对噪声的稳定性.
- ARMGA 实现了优越的集群性能,并提高了效率.
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