Communication Theoretic Data Analytics

TL;DR

This paper introduces a communication theory-based framework for data analytics, modeling data as social networks and applying information theory to optimize data transfer and reduce dimensionality, demonstrated through financial data and pattern recognition.

Contribution

It extends communication and information theory to data analytics by modeling data as social networks and developing methods for information transfer optimization and dimensionality reduction.

Findings

Equalizer improves information transfer in financial data

Information geometry aids in effective dimensionality reduction

Potential applications span various data analytics fields

Abstract

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.