Nonparametric Bayesian label prediction on a graph
Nonparametric Bayesian label prediction on a graph
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DOI：
10.1016/j.csda.2017.11.008
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Jarno Hartog;H. Zanten
中科院分区：
文献类型：
--
作者：
Jarno Hartog;H. Zanten
描述了一种非参数贝叶斯方法在图上解决二值分类问题的实现。考虑了随机缩放高斯先验的分层贝叶斯方法。先验算法使用图拉普拉斯算子来考虑图的底层几何。提出了一种基于理论最优先验和基于部分共轭的更灵活变体的方法。用两个模拟数据算例和两个实际数据算例来说明所提出的方法。
An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a theoretically optimal prior and a more flexible variant using partial conjugacy are proposed. Two simulated data examples and two examples using real data are used in order to illustrate the proposed methods.