Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search

Ninh Pham, Tao Liu

Keywords: Approximate nearest neighbor search, locality-sensitive, recall-speed tradeoff

Abstract:

We present Falconn++, a novel locality-sensitive filtering (LSF) approach for approximate nearest neighbor search on angular distance.
Falconn++ can filter out potential far away points in any hash bucket before querying, which results in higher quality candidates compared to other hashing-based solutions. Theoretically, Falconn++ asymptotically achieves lower query time complexity than Falconn, an optimal locality-sensitive hashing scheme on angular distance. Empirically, Falconn++ achieves a higher recall-speed tradeoff than Falconn on many real-world data sets. Falconn++ is also competitive with HNSW, an efficient representative of graph-based solutions on high search recall regimes.

TL;DR: We present Falconn++, a novel locality-sensitive filtering approach for approximate nearest neighbor search on angular distance.

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/falconn-a-locality-sensitive-filtering/code)