Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
Eyal Kushilevitz, Rafail Ostrovsky, Yuval Rabani
Abstract:
We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a space-efficient data structure that would allow us to search, given a query vector, for the closest or nearly closest vector in the database. We also address this problem when distances are measured by the L1 norm, and in the Hamming cube. Significantly improving and extending recent results of Kleinberg, we construct data structures whose size is polynomial in the size of the database, and search algorithms that run in time nearly linear or nearly quadratic in the dimension (depending on the case; the extra factors are polylogarithmic in the size of the database).
comment: Journal version appeared in SIAM J. Comput. 30(2): 457-474 (2000). Preliminary version in Proceedings of The 30's ACM Symposium on Theory of Computing (STOC-98)
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