Sublinear Time Nearest Neighbor Search over Generalized Weighted Manhattan Distance
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Nearest Neighbor Search (NNS) over generalized weighted distance is fundamental to a wide range of applications. The problem of NNS over the generalized weighted Square Euclidean distance has been studied in previous work. However, numerous studies have shown that the Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS. To the best of our knowledge, no prior work presents a sublinear time solution to the problem of NNS over the generalized weighted Manhattan distance. In this paper, we propose two novel sublinear time hashing schemes (d_w^l_1,l_2)-ALSH and (d_w^l_1,θ)-ALSH to solve the problem.
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