I created a useful LSH algorithm for real-valued vector data. Its strong suit is that it is deterministic. In other words, it is deterministically guaranteed to

I created a useful LSH algorithm for real-valued vector data. Its strong suit is that it is deterministic. In other words, it is deterministically guaranteed to find all close-enough neighbors, and omit all far-enough neighbors. Most other LSH algorithms do not provide both of these guarantees deterministically -- instead they strive toward those goals probabilistically. It's also fast, conceptually simple, and built on top of a very cool non-obvious tessellation of arbitrarily high dimensions. Here are some slides on it: https://files.pbworks.com/download/dJjN51z5uR/hackerdojo/27189819/lsh_hacker_dojo_talk.pdf https://files.pbworks.com/download/dJjN51z5uR/hackerdojo/271... This work was also published in SODA 10: https://www.siam.org/proceedings/soda/2010/SODA10_094_neylont.pdf https://www.siam.org/proceedings/soda/2010/SODA10_094_neylon...