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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
73/2017

Computing the Unique Information

Pradeep Kumar Banerjee, Johannes Rauh and Guido Montúfar

Abstract

Given a set of predictor variables and a response variable, how much information do the predictors have about the response, and how is this information distributed between unique, complementary, and shared components? Recent work has proposed to quantify the unique component of the decomposition as the minimum value of the conditional mutual information over a constrained set of information channels. We present an efficient iterative divergence minimization algorithm to solve this optimization problem with convergence guarantees, and we evaluate its performance against other techniques.

Received:
09.11.17
Published:
14.11.17
MSC Codes:
94A15
Keywords:
Positive information decomposition, mutual information, alternating divergence minimization

Related publications

inBook
2018 Repository Open Access
Pradeep Kumar Banerjee, Johannes Rauh and Guido Montúfar

Computing the unique information

In: IEEE international symposium on information theory (ISIT) from June 17 to 22, 2018 at the Talisa Hotel in Vail, Colorado, USA
Piscataway, NY : IEEE, 2018. - pp. 141-145