We study locality-sensitive hash methods for the nearest neighbor problem for
the angular distance, focusing on the approach of first projecting down onto a
low-dimensional subspace, and then partitioning the projected vectors according
to Voronoi cells induced by a suitable spherical code. This approach
generalizes and interpolates between the fast but suboptimal hyperplane hashing
of Charikar [STOC’02] and the asymptotically optimal but practically often
slower hash families of Andoni-Indyk [FOCS’06], Andoni-Indyk-Nguyen-Razenshteyn
[SODA’14] and Andoni-Indyk-Laarhoven-Razenshteyn-Schmidt [NIPS’15]. We set up a
framework for analyzing the performance of any spherical code in this context,
and we provide results for various codes from the literature, such as those
related to regular polytopes and root lattices. Similar to hyperplane hashing,
and unlike cross-polytope hashing, our analysis of collision probabilities and
query exponents is exact and does not hide order terms which vanish only for
large