Discovering Structure in High-Dimensional Data Through Correlation Explanation
This provides an unsupervised, scalable method for structure discovery in high-dimensional data, which is incremental as it builds on information-theoretic principles without introducing a new paradigm.
The paper tackles the problem of learning hierarchical abstract representations from high-dimensional data by optimizing an information-theoretic objective to explain correlations, and demonstrates that Correlation Explanation (CorEx) automatically discovers meaningful structure in diverse datasets like personality tests, DNA, and human language.
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.