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/271...

This work was also published in SODA 10:

https://www.siam.org/proceedings/soda/2010/SODA10_094_neylon...