Graph Learning Information Criterion
Graph Learning Information Criterion
复制标题
图学习信息准则
DOI：
10.1109/icassp43922.2022.9746309
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Tanaka Yuichi
中科院分区：
文献类型：
--
作者：
Yamada Koki;Tanaka Yuichi
本文提出了一种用于图学习的参数选择方法。图学习是一种从观测中学习图的技术，在许多应用中都是必需的，例如分类、预测和聚类。然而，没有确定的方法来确定控制反映先验知识的正则化强度的超参数。为了解决这个问题，我们考虑了一种基于拉普拉斯约束高斯马尔可夫随机场的图学习问题的模型选择准则。所提出的准则是基于模型证据的值，用于贝叶斯统计中的模型选择。它可以通过对图学习模型的后验分布的负对数似然进行平均来估计。为了计算这一准则，我们提出了一种有效的后验分布采样器。在随机图的实验中，我们证明了所提出的方法可以在F-度量和相对误差之间选择一个很好的折中的超参数。
In this paper, we propose a parameter selection method for graph learning. Graph learning, a technique of learning graphs from observations, is required in many applications, e.g., classification, prediction, and clustering. However, there is no established method to determine hyperparameters that control the strength of the regularization reflecting prior knowledge. To resolve the problem, we consider a model selection criterion for the graph learning problem based on Laplacian constrained Gaussian Markov random field. The proposed criterion is the value based on model evidence, which is used for model selection in Bayesian statistics. It can be estimated by averaging the negative log-likelihood over the posterior distribution of a graph learning model. To compute this criterion, we present an efficient sampler of the posterior distribution. In the experiment with random graphs, we demonstrate that the proposed method can select hyperparameters having a good trade-off between F-measure and relative error.