RG Smoothing Algorithm Which Makes Data Compression

TL;DR

This paper introduces a novel smoothing algorithm based on renormalization group theory that enables data compression and recursive processing, with potential applications in pattern recognition and data compression.

Contribution

It presents a new smoothing method that combines data compression and recursive implementation, distinct from existing techniques.

Findings

Algorithm is simple and easy to implement in hardware.

Uses only one free parameter: number of iterative steps.

Potential applications in pattern recognition and data compression.

Abstract

I describe a new method for smoothing a one-dimensional curve in Euclidian space with an arbitrary number of dimensions. The basic idea is borrowed from renormalization group theory which previously was applied to biological macromolecules. There are two crucial differences from other smoothing methods which make the algorithm unique: data compression and recursive implementation. One of the simplest forms of the method that is described in this article has only one free parameter - the number of iterative steps. This means that hardware implementation should be relatively easy because each loop is simple and strictly defined. The method could be beneficially applied to pattern recognition and data compression in future studies.