Information-Theoretic Inequalities on Unimodular Lie Groups

TL;DR

This paper extends classical information-theoretic inequalities to unimodular Lie groups, enabling analysis of information processes in noncommutative group settings relevant to engineering and robotics.

Contribution

It develops new inequalities and extends key information theory concepts from Euclidean space to unimodular Lie groups, including definitions like Fisher information.

Findings

Derived fifteen inequalities analogous to classical information theory.

Extended Fisher information and convolution concepts to Lie groups.

Applicable to problems in robotics, image reconstruction, and biological information processing.

Abstract

Classical inequalities used in information theory such as those of de Bruijn, Fisher, and Kullback carry over from the setting of probability theory on Euclidean space to that of unimodular Lie groups. These are groups that posses integration measures that are invariant under left and right shifts, which means that even in noncommutative cases they share many of the useful features of Euclidean space. In practical engineering terms the rotation group and Euclidean motion group are the unimodular Lie groups of most interest, and the development of information theory applicable to these Lie groups opens up the potential to study problems relating to image reconstruction from irregular or random projection directions, information gathering in mobile robotics, satellite attitude control, and bacterial chemotaxis and information processing. Several definitions are extended from the Euclidean case to that of Lie groups including the Fisher information matrix, and inequalities analogous to those in classical information theory are derived and stated in the form of fifteen small theorems. In all such inequalities, addition of random variables is replaced with the group product, and the appropriate generalization of convolution of probability densities is employed.