Experimental Analysis of Locality Sensitive Hashing Techniques for High-Dimensional Approximate Nearest Neighbor Searches

TL;DR

This paper experimentally compares the performance of different Locality Sensitive Hashing techniques for high-dimensional approximate nearest neighbor searches, focusing on algorithm time and index I/O costs.

Contribution

It provides a comprehensive experimental analysis of LSH techniques, highlighting that C2LSH remains the most effective in terms of performance and accuracy.

Findings

C2LSH outperforms recent competitors in speed and accuracy

Both algorithm time and index I/O are critical for LSH performance

C2LSH maintains state-of-the-art performance across datasets

Abstract

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many multimedia retrieval applications. Exact tree-based indexing approaches are known to suffer from the notorious curse of dimensionality for high-dimensional data. Approximate searching techniques sacrifice some accuracy while returning good enough results for faster performance. Locality Sensitive Hashing (LSH) is a very popular technique for finding approximate nearest neighbors in high-dimensional spaces. Apart from providing theoretical guarantees on the query results, one of the main benefits of LSH techniques is their good scalability to large datasets because they are external memory based. The most dominant costs for existing LSH techniques are the algorithm time and the index I/Os required to find candidate points. Existing works do not compare both of these dominant costs in their evaluation. In this experimental survey paper, we show the impact of both these costs on the overall performance of the LSH technique. We compare three state-of-the-art techniques on four real-world datasets, and show that, in contrast to recent works, C2LSH is still the state-of-the-art algorithm in terms of performance while achieving similar accuracy as its recent competitors.