Estimating mutual information in high dimensions via classification error.

Charles Y. Zheng, Yuval Benjamini

Research output: Working paper/preprintPreprint


Multivariate pattern analyses approaches in neuroimaging are fundamentally concerned with investigating the quantity and type of information processed by various regions of the human brain; typically, estimates of classification accuracy are used to quantify information. While a extensive and powerful library of methods can be applied to train and assess classifiers, it is not always clear how to use the resulting measures of classification performance to draw scientific conclusions: e.g. for the purpose of evaluating redundancy between brain regions. An additional confound for interpreting classification performance is the dependence of the error rate on the number and choice of distinct classes obtained for the classification task. In contrast, mutual information is a quantity defined independently of the experimental design, and has ideal properties for comparative analyses. Unfortunately, estimating the mutual information based on observations becomes statistically infeasible in h
Original languageEnglish
StatePublished - 2016

Publication series

NamearXiv preprint arXiv:1606.05229


  • Statistics - Machine Learning
  • Computer Science - Information Theory


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