论文标题
恢复有条件独立图的方法：调查
Methods for Recovering Conditional Independence Graphs: A Survey
论文作者
论文摘要
条件独立性（CI）图是一种概率图形模型，主要用于获得有关特征关系的见解。每个边缘代表连接特征之间的部分相关，该特征提供了有关其直接依赖性的信息。在这项调查中，我们列出了不同的方法，并研究了恢复CI图的技术的进步。我们涵盖了传统的优化方法，以及最近开发的深度学习体系结构及其建议的实施。为了促进更广泛的采用，我们包括巩固相关操作的前预述，例如获得混合数据类型的协方差矩阵的技术。
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations. To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes.
