Discovering Structure in High-Dimensional Data Through Correlation Explanation
Abstract
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.
Cite
@article{arxiv.1406.1222,
title = {Discovering Structure in High-Dimensional Data Through Correlation Explanation},
author = {Greg Ver Steeg and Aram Galstyan},
journal= {arXiv preprint arXiv:1406.1222},
year = {2014}
}
Comments
15 pages, 6 figures. Includes supplementary material and link to code. Published in the proceedings of the 28th Annual Conference on Neural Information Processing Systems, NIPS 2014