对于图形卷积网络的随机投影森林初始化
Mashaan Alshammari1, John Stavrakakis2, Adel F Ahmed3
1Independent Researcher, Riyadh, Saudi Arabia.
MethodsX
|August 21, 2023
概括
用随机投影森林 (rpForest) 初始化图形卷积网络 (GCNs) 提高了对k-最近邻 (k-nn) 图形的性能. rpForest赋予不同的边缘权重,更好地表示样本相似性,以在图表上增强深度学习.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 图形卷积网络 (GCNs) 将深度学习扩展到图形结构数据.
- GCN通常需要图形结构和特征矩阵作为输入.
- 通常,只有特征矩阵可用,需要像k-近邻 (k-nn) 这样的图形构造方法.
研究的目的:
- 提出和评估一种新的方法来初始化GCNs当缺少图形结构时.
- 通过使用带有不同边重的图形表示来提高GCN性能.
- 引入随机投影森林 (rpForest) 用于构建信息图的初始化.
主要方法:
- 使用随机投影森林 (rpForest) 构建图形,根据样本相似性分配不同的边缘重量.
- 使用rpForest构建的图形初始化GCN.
- 使用光谱分析来确定rpForest的最佳树木数量超参数.
主要成果:
- rp基于森林的图形初始化显著优于GCNs的k-nn初始化.
- 在rpForest图中变化的边缘重量更好地捕捉样本相似性,更有效地指导GCN训练.
- 光谱分析提供了一个设置rpForest超参数 (树木数) 的方法.
结论:
- 与传统的k-nn方法相比,rpForest为GCN提供了一种优越的图形初始化方法.
- rpForest能够模拟细微的样本相似性的能力,增强了对图形数据的深度学习.
- 拟议的光谱分析方法有助于对rpForest.com进行可靠的超参数选择.
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