Abstract:Locality sensitive hashing (LSH) is a powerful tool in data science, which enables sublinear-time approximate nearest neighbor search. A variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at query time. In this paper, we propose multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities. mp-LSH supports L2, cosine, and inner product … Show more
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