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MiS Preprint

Computing the Unique Information

Pradeep Kumar Banerjee, Johannes Rauh and Guido Montúfar


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.

Nov 9, 2017
Nov 14, 2017
MSC Codes:
Positive information decomposition, mutual information, alternating divergence minimization

Related publications

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