对于不完整的多视图数据集群的子图传播和对比校准
IEEE transactions on neural networks and learning systems
|January 18, 2024
概括
本研究引入了一个深度集群框架 (SPCC) 来解决多视图数据集中缺失的数据. 通过重建图形结构和对准集群分布以提高准确性,SPCC有效地挖掘不完整的多视图数据.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 多视图数据挖掘至关重要,但由于噪音和收集失败而受到不完整属性的挑战.
- 现有的方法在缺少数据方面扎,未能对跨视图共享信息的补充表示进行校准.
- 一个重要的问题是不完整的多视图数据的潜在空间中的集群分布不对齐问题 (CDUP).
研究的目的:
- 为不完整的多视图原始数据提出一个新的深度聚类框架,子图传播和对比校准 (SPCC).
- 解决采矿拓在缺少多视图数据和校准补充表示的挑战.
- 在不完整的多视图数据的潜在空间中解决集群分布不对齐问题 (CDUP).
主要方法:
- 重建一个全局结构图,通过从每个视图中的完整数据中传播子图.
- 以全球结构图为指导的缺失视图的补充和校准,并使用视图之间的对比学习.
- 在不同的视图中对齐补充的集群分布,使用对比学习 (CL) 来解决CDUP.
主要成果:
- 拟议的SPCC框架在六个基准数据集上展示了先进的性能.
- 该方法有效地解决了不完整的多视图数据的挑战,包括拓挖掘和表示校准.
- 验证证实了SPCC方法在多视图集群任务中的有效性和优越性.
结论:
- 该SPCC框架提供了一个强大的解决方案,用于集群不完整的多视图数据.
- 子图传播和对比校准是处理缺失数据和对准隐藏表示的有效策略.
- 该研究验证了SPCC方法在复杂的多视图数据挖掘场景中实现卓越性能的能力.
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