  We propose a new class of data-independent locality-sensitive hashing (LSH)
algorithms based on the fruit fly olfactory circuit. The fundamental difference
of this approach is that, instead of assigning hashes as dense points in a low
dimensional space, hashes are assigned in a high dimensional space, which
enhances their separability. We show theoretically and empirically that this
new family of hash functions is locality-sensitive and preserves rank
similarity for inputs in any `p space. We then analyze different variations on
this strategy and show empirically that they outperform existing LSH methods
for nearest-neighbors search on six benchmark datasets. Finally, we propose a
multi-probe version of our algorithm that achieves higher performance for the
same query time, or conversely, that maintains performance of prior approaches
while taking significantly less indexing time and memory. Overall, our approach
leverages the advantages of separability provided by high-dimensional spaces,
while still remaining computationally efficient
