稀疏的低级多视图子空间集群与共识和统一的双分位图
IEEE transactions on neural networks and learning systems
|November 22, 2023
概括
本研究介绍了SL-CAUBG,这是一个新的多视图子空间集群 (MVSC) 框架. 它产生共识和统一的双边图,通过捕获本地和全球数据结构来改善聚类.
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:
- 多视图子空间集群 (MVSC) 经常使用技术来减少复杂性.
- 现有的方法受到独立采样的和密集的两部分图的困扰,阻碍了准确的数据表征和局部结构捕获.
研究的目的:
- 为了解决当前MVSC选和图形构造的局限性.
- 开发一个框架,有效地整合信息跨多个视图,以提高集群性能.
主要方法:
- 拟议的SL-CAUBG框架在所有观点之间产生共识,避免抽样.
- 从共识直接构建了一个统一的双边图形,消除了对图形融合的需求.
- 采用L1-norm和Laplacian-rank约束来捕获本地和全球数据结构.
主要成果:
- 在L1规范约束促进稀疏的链接,澄清-样本关系.
- 拉普拉西安等级约束通过图形连接提取全球特征.
- 代重权和牛顿的方法解决了非可区分的目标,而凸的痕迹约束处理了非凸性.
结论:
- SL-CAUBG在多视图子空间集群中表现出卓越的性能.
- 该框架有效地通过共识和统一的双边图表捕捉了基本的本地和全球数据结构.
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