@MISC{Kapralov_smoothtradeoffs, author = {Michael Kapralov}, title = {Smooth Tradeoffs between Insert and Query Complexity in Nearest Neighbor Search}, year = {} }
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Abstract
Abstract Locality Sensitive Hashing (LSH) has emerged as the method of choice for high dimensional similarity search, a classical problem of interest in numerous applications. LSH-based solutions require that each data point be inserted into a number A of hash tables, after which a query can be answered by performing B lookups. The original LSH solution of In this paper, we present an algorithm for performing similarity search under the Euclidean metric that resolves the problem above. Our solution is inspired by Entropy LSH, but uses a very different analysis to achieve a smooth tradeoff between insert and query complexity. Our results improve upon or match, up to lower order terms in the exponent, best known data-oblivious algorithms for main memory LSH for the Euclidean metric.