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- W2024409132 abstract "This paper proposes new methods to answer approximate nearest neighbor queries on a set of n points in d -dimensional Euclidean space. For any fixed constant d , a data structure with O( $varepsilon$ (1-d)/2 n log n) preprocessing time and O( $varepsilon$ (1-d)/2 log n) query time achieves an approximation factor 1+ $varepsilon$ for any given 0 < $varepsilon$ < 1; a variant reduces the $varepsilon$ -dependence by a factor of $varepsilon$ -1/2 . For any arbitrary d , a data structure with O(d 2 n log n) preprocessing time and O(d 2 log n) query time achieves an approximation factor O(d 3/2 ) . Applications to various proximity problems are discussed." @default.
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- W2024409132 title "Approximate Nearest Neighbor Queries Revisited" @default.
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