Towards a Model for LSH

TL;DR

This paper discusses modeling the properties of locality-sensitive hashing (LSH) to improve neighbor search efficiency in clustering and outlier detection amidst growing data volumes and high dimensionality.

Contribution

It proposes directions to model the properties of LSH, aiming to enhance approximate neighbor search methods for large, high-dimensional datasets.

Findings

Identifies limitations of classical index structures in high dimensions

Highlights the potential of LSH for faster neighbor search with low error rates

Suggests modeling approaches for LSH properties to optimize performance

Abstract

As data volumes continue to grow, clustering and outlier detection algorithms 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. Locality-sensitive hashing is one of those. We indicate directions to model the properties of LSH.