MP-RW-LSH: An Efficient Multi-Probe LSH Solution to ANNS in L_1 Distance
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Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-sensitive hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS-L_2, a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L_1 distance. Another contribution of this work is to explain why a state-of-the-art ANNS-L_1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. We show that MP-RW-LSH uses 15 to 53 times fewer hash tables than CP-LSH for achieving similar query accuracies.
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