使用张量固有值分解的超图分区
Deepak Maurya1, Balaraman Ravindran1
1Computer Science and Engineering, Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India.
PloS one
|July 21, 2023
概括
本研究引入了一种新的方法来分割k-均的超图,使用张量表示来捕捉复杂的相互作用. 该方法改进了基于图形的方法,通过保留必要的超图形信息来获得更好的分区结果.
科学领域:
- 机器学习 机器学习
- 图形理论 图形理论
- 数据挖掘 数据挖掘
背景情况:
- 超图提供了一种比传统图表更全面的方式来建模复杂的关系.
- 现有的超图分割方法经常通过将超图减少为图形而丢失信息.
研究的目的:
- 提出一种新的基于张数的方法来分割k-均的超图.
- 克服图形缩小方法在捕获超二极交互方面的局限性.
主要方法:
- 使用基于张量表征的超图来捕捉超二极交互.
- 使用拉普拉斯张量固有值分解将图形切割概念 (min-ratio-cut, normalized-cut) 扩展到超图.
- 开发一个由光谱图理论启发的超边形分区算法,并引入一个"超边缘分数"度量.
主要成果:
- 拟议的方法有效地捕捉了超二相互作用,优于图形缩小技术.
- 对于偶序超图拉普拉斯张数的最小正自值,我们得出了一个更紧的上限.
- 对合成超图的数值实验证明了拟议的分区方法的有效性.
结论:
- 与现有方法相比,基于张数的方法为超图分割提供了一个优越的框架.
- 这种新的配方增强了通过超图表示的复杂关系数据分析和分区的能力.
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