|
Abstract : |
Yuval Rabani z We address the problem of designing data structures that allow e cient search for approximate nearest neighbors. More speci cally, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a space-e cient 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. Signi cantly 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)., |
