CorEx | Correlation Explanation discovers a hierarchy | Machine Learning library

CorEx is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. CorEx has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However CorEx build file is not available. You can download it from GitHub.

The principle of Cor-relation Ex-planation has recently been introduced as a way to build rich representations that are informative about relationships in data. This project consists of python code to build these representations. The version here implements only the technique of the 2014 NIPS paper. A version that incorporates features like continuous variables, missing values, and Bayesian smoothing is now available here: It subsumes all functionality in this version. Despite the name, there's nothing specific to biology about it, but development was guided by problems in biomedical domains. A preliminary version of the technique is described in this paper. Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg and Aram Galstyan, NIPS 2014, Some theoretical developments are described here: Maximally Informative Hierarchical Representions of High-Dimensional Data Greg Ver Steeg and Aram Galstyan, AISTATS 2015, The code here is written by Greg Ver Steeg and Gabriel Pereyra.