# Mathematical Models for Local Sensing Hashes

arXiv: https://arxiv.org/abs/2111.08344 · HTML: https://mlsanity.com/paper/2111.08344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 104 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [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)
- [I/O-Efficient Similarity Join](https://mlsanity.com/paper/1507.00552/) (2015)

## Abstract (arXiv)

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.