Methods for Recovering Conditional Independence Graphs: A Survey
It provides a comprehensive overview for researchers and practitioners working with probabilistic graphical models to understand feature relationships, but it is incremental as it surveys existing methods without introducing new ones.
This survey compiles and examines various methods for recovering Conditional Independence graphs, covering traditional optimization techniques and recent deep learning architectures, along with practical implementations and preliminaries to aid adoption.
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.