通过可靠的视图指导进行未配对的多视图集群
IEEE transactions on neural networks and learning systems
|November 6, 2024
概括
本研究引入了未配对的多视图集群 (UMC) 的新方法,克服了在没有配对样本的情况下数据对齐的挑战. 提出的可靠的视图引导方法显著提高了聚类准确性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机视觉 计算机视觉
背景情况:
- 未配对的多视图集群 (UMC) 具有挑战性,因为视图之间没有配对的样本.
- 现有的不完整的多视图集群 (IMC) 方法通常依赖于样本配对,这对于UMC来说是不可行的.
- 由于不确定的集群结构和配对关系,UMC面临着困难.
研究的目的:
- 为未配对的多视图数据开发有效的联合集群方法.
- 在多个视图中提取一致的集群结构,尽管缺乏配对的观测.
- 解决UMC中不确定的集群结构和配对关系的挑战.
主要方法:
- 建议的可靠视图引导UMC (RG-UMC) 和多个可靠视图 (RGs-UMC) 方法.
- 设计了对齐模块,以使用可靠的视图指导优化.
- 纳入了集群内部样本关系的紧性模块,以及对歧视性特征的直角约束.
主要成果:
- 与最先进的方法相比,RG-UMC和RGs-UMC表现出优越的性能.
- 在正常化的相互信息 (NMI) 中观察到显著改善:RG-UMC的24.14%和RGs-UMC的29.42%.
- 这些方法有效地应对多视图集群中的未配对数据的挑战.
结论:
- 可靠的视图引导方法为未配对的多视图集群提供了强大的解决方案.
- 提出的方法成功地提取了一致的集群结构,并提高了集群性能.
- 通过利用可靠的观点进行指导和调整,可以有效地解决UMC.
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