# Towards a Model for LSH

arXiv: https://arxiv.org/abs/2105.05130 · HTML: https://mlsanity.com/paper/2105.05130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 88 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 2 of 2)

- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Adaptive MapReduce Similarity Joins](https://mlsanity.com/paper/1804.05615/) (2018)

## Abstract (arXiv)

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.