Mathematical Models for Local Sensing Hashes

TL;DR

This paper discusses the development of mathematical models for local sensing hashes, aiming to improve approximate neighbor search efficiency in high-dimensional data by addressing the limitations of classical indexing methods.

Contribution

It introduces a framework for mathematically modeling local sensing hashes, providing insights into their properties for enhanced data search algorithms.

Findings

Proposes a mathematical approach to model local sensing hashes.

Highlights potential for faster neighbor searches in high-dimensional data.

Suggests directions for future research in approximate indexing structures.

Abstract

As data volumes continue to grow, searches in data are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Local sensing hashes is one of those. We indicate directions to mathematically model the properties of it.