@article{
 title = {Discovering Structure in High-Dimensional Data Through Correlation Explanation},
 type = {article},
 id = {a1827df7-2a70-3446-9a24-1b0ffeff365a},
 created = {2017-01-16T21:19:40.000Z},
 accessed = {2016-09-08},
 file_attached = {true},
 profile_id = {3c7f894b-393d-3db5-a8d3-2213ffea6343},
 group_id = {3b2f1ff0-ed26-3f63-8337-e926685fa861},
 last_modified = {2017-01-16T21:20:09.000Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Steeg},
 abstract = {We introduce a method to learn a hierarchy of successively more abstract repre-sentations of complex data based on optimizing an information-theoretic objec-tive. Intuitively, the optimization searches for a set of latent factors that best ex-plain 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) auto-matically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language.},
 bibtype = {article},
 author = {Ver Steeg, Greg and Galstyan, Aram}
}

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