论文标题
使用非高斯性的因果定向无环图估计
Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity
论文作者
论文摘要
已经提出了许多方法来发现机器学习和数据挖掘中的因果关系。其中，最先进的Var-Lingam（矢量自动回归线性非高斯无环模型的缩写）是一种理想的方法，可以揭示瞬时和时置关系。但是，需要分析所有获得的VAR矩阵以推断最终因果图，从而导致参数数量增加。为了解决这个问题，我们提出了CGP-Lingam（因果图过程-Lingam的缩写），该问题的模型参数明显较少，并且仅处理一个因果图，用于通过利用图形信号处理（GSP）来解释因果关系。
Numerous approaches have been proposed to discover causal dependencies in machine learning and data mining; among them, the state-of-the-art VAR-LiNGAM (short for Vector Auto-Regressive Linear Non-Gaussian Acyclic Model) is a desirable approach to reveal both the instantaneous and time-lagged relationships. However, all the obtained VAR matrices need to be analyzed to infer the final causal graph, leading to a rise in the number of parameters. To address this issue, we propose the CGP-LiNGAM (short for Causal Graph Process-LiNGAM), which has significantly fewer model parameters and deals with only one causal graph for interpreting the causal relations by exploiting Graph Signal Processing (GSP).
