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• W2951721808 abstract "We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation. Specifically, for every $n$, $d = n^{o(1)}$, and every $d$-dimensional symmetric norm $|cdot|$, there exists a data structure for $mathrm{poly}(log log n)$-approximate nearest neighbor search over $|cdot|$ for $n$-point datasets achieving $n^{o(1)}$ query time and $n^{1+o(1)}$ space. The main technical ingredient of the algorithm is a low-distortion embedding of a symmetric norm into a low-dimensional iterated product of top-$k$ norms. We also show that our techniques cannot be extended to general norms." @default.
• W2951721808 created "2019-06-27" @default.
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• W2951721808 date "2016-11-18" @default.
• W2951721808 modified "2023-10-17" @default.
• W2951721808 title "Approximate Near Neighbors for General Symmetric Norms" @default.
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