# Discovering Structure in High-Dimensional Data Through Correlation Explanation

arXiv: https://arxiv.org/abs/1406.1222 · HTML: https://mlsanity.com/paper/1406.1222/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2014.

## Built on by (most cited first, 5 of 5)

- [Cancer Gene Profiling through Unsupervised Discovery](https://mlsanity.com/paper/2102.07713/) (2021)
- [Exploring higher-order neural network node interactions with total correlation](https://mlsanity.com/paper/2402.04440/) (2024)
- [ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification](https://mlsanity.com/paper/2501.14785/) (2025)
- [Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics](https://mlsanity.com/paper/2505.16493/) (2025)
- [Maximum Total Correlation Reinforcement Learning](https://mlsanity.com/paper/2505.16734/) (2025)

## Abstract (arXiv)

We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual information. The method is unsupervised, requires no model assumptions, and scales linearly with the number of variables which makes it an attractive approach for very high dimensional systems. We demonstrate that Correlation Explanation (CorEx) automatically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language.