DLITE: The Discounted Least Information Theory of Entropy

TL;DR

DLITE introduces a new entropy-based measure that combines key properties of classical information metrics and satisfies metric conditions, advancing information theory and its applications.

Contribution

The paper presents DLITE, a novel entropy measure that fulfills both informational and metric properties, filling a gap in existing information measures.

Findings

DLITE exhibits key characteristics of an information measure.

DLITE satisfies metric conditions, unlike some classical measures.

The measure bridges gaps in information theory applications.

Abstract

We propose an entropy-based information measure, namely the Discounted Least Information Theory of Entropy (DLITE), which not only exhibits important characteristics expected as an information measure but also satisfies conditions of a metric. Classic information measures such as Shannon Entropy, KL Divergence, and Jessen-Shannon Divergence have manifested some of these properties while missing others. This work fills an important gap in the advancement of information theory and its application, where related properties are desirable.