基于图形的多中心非负矩阵因子化
IEEE transactions on neural networks and learning systems
|November 28, 2023
概括
这项研究引入了多中心体NMF (MCNMF) 以改进数据聚类. 通过将数据点与相邻的中心体表示,MCNMF有效地处理复杂的数据几何形状,提高了集群精度.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算几何学的计算几何学
背景情况:
- 非负矩阵分解 (NMF) 是一种标准的数据表示技术.
- 传统的基于NMF的集群与复杂的数据几何相斗争,原因是单个中心的限制.
- 现有的方法无法在数据点中保存局部几何结构.
研究的目的:
- 提出一种新的基于多中心的聚类方法,即基于图的多中心NMF (MCNMF).
- 通过保留本地几何结构和改进样本成员身份识别来增强数据聚类.
- 在处理复杂的数据分布时克服单中心型NMF的局限性.
主要方法:
- 在数据点和中心点之间构建一个邻里连接图.
- 以其相邻的中心体来表示每个数据点,以保持本地几何.
- 构建一个非定向连接的中心体图,以形成中心体群.
- 根据已识别的中心点群重建会员指数矩阵.
主要成果:
- 在复杂的几何结构中,MCNMF有效地处理数据点.
- 该方法保留了对于准确聚类至关重要的局部几何信息.
- 在合成和基准数据集上的实验结果证明了MCNMF的卓越性能.
- MCNMF的性能优于传统的基于单个中心的方法.
结论:
- 拟议的MCNMF方法为基于NMF的聚类提供了显著的进步.
- 通过使用多中心体表示,MCNMF准确地对具有复杂几何形状的数据进行集群.
- 该方法为集群任务中的样本成员身份识别提供了强大的解决方案.
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