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We have decided to discontinue the publication of preprints on our preprint server end of 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.
Computing the Unique Information
Pradeep Kumar Banerjee, Johannes Rauh and Guido MontúfarAbstract
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
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, USAPiscataway, NY : IEEE, 2018. - pp. 141-145