A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.

ABSTRACT: The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimen

SUBMITTER: Marrelec G 

PROVIDER: S-EPMC4583305 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature