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Cited by 259

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Efficient search for approximate nearest neighbor in high dimensional spaces

Open access
Kushilevitz, Eyal, Ostrovsky, Rafail, Rabani, Yuval·1998·Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98

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 struct,ure t,hat would allow us to search, given a query vector, for t.he closest or nearly closest vector in the database.We also address t.hii problem when distances are measured by the L1 norm, and in t.he Hamming cube.Sign%cant.lyimproving and extending recent results of Kleinberg, we const,ruct data structures whose size is polynomial in the size of t,he database, and search algorithms t,hat run in time nearly linear or nearly quadratic in the dimension (depending on the case; the extra factors are polylogarit.hmicin the size of the database).

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Metadata

Authors
Kushilevitz, E., Ostrovsky, R., & Rabani, Y.
Venue
Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98
Publisher
ACM Press
Published
1998
Pages
614-623
OpenAlex
W2050749090
Cited by
259
References
3
FWCI
23.19
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Efficient search for approximate nearest neighbor in high dimensional spaces. https://app.cochat.ai/papers/10.1145/276698.276877
The paper
Kushilevitz, E., Ostrovsky, R., & Rabani, Y. (1998). Efficient search for approximate nearest neighbor in high dimensional spaces. Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98. https://doi.org/10.1145/276698.276877

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