Towards a Model for LSH
This work is incremental, as it builds on existing LSH methods to potentially improve efficiency for data analysis tasks in large-scale applications.
The paper addresses the challenge of time-consuming clustering and outlier detection in high-dimensional data by proposing directions to model the properties of locality-sensitive hashing (LSH) to accelerate neighbor search with low error rates, though no specific results or numbers are provided.
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.