Publication
IEEE Trans. Inf. Theory
Paper

Principal Inertia Components and Applications

View publication

Abstract

We explore properties and applications of the principal inertia components (PICs) between two discrete random variables $X$ and $Y$. The PICs lie in the intersection of information and estimation theory, and provide a fine-grained decomposition of the dependence between $X$ and $Y$. Moreover, the PICs describe which functions of $X$ can or cannot be reliably inferred (in terms of MMSE), given an observation of $Y$. We demonstrate that the PICs play an important role in information theory, and they can be used to characterize information-theoretic limits of certain estimation problems. In privacy settings, we prove that the PICs are related to the fundamental limits of perfect privacy.

Date

01 Aug 2017

Publication

IEEE Trans. Inf. Theory

Authors

Share