[gajjanag]

By the way, https://github.com/FALCONN-LIB/FALCONN contains a really good LSH implementation. Also see https://www.mit.edu/~andoni/LSH/ if you want to know more about the research literature.

The fastest way for Euclidean space that I know that works well in practice is via Leech lattice decoding: https://www.semanticscholar.org/paper/Maximum-likelihood-dec... , or https://www.semanticscholar.org/paper/Efficient-bounded-dist... .

It is possible to create an implementation based on the above that decodes 24 dimensional points to the closest Leech lattice vector in < 1 microsecond per point on my AMD Ryzen laptop. Combine with some fast random projections/Johnson Lindenstrauss as described in the article to form the LSH.

This LSH family is unfortunately not present in FALCONN, but the alternatives in FALCONN are pretty good.

Source: thought extensively about LSH for my PhD.

[sendfoods]

FALCONN doesn't look maintained, unfortunately