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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Locality-sensitive hashing ： ウィキペディア英語版 Locality-sensitive hashing Locality-sensitive hashing (LSH) reduces the dimensionality of high-dimensional data. LSH hashes input items so that similar items map to the same “buckets” with high probability (the number of buckets being much smaller than the universe of possible input items). LSH differs from conventional and cryptographic hash functions because it aims to maximize the probability of a “collision” for similar items. Locality-sensitive hashing has much in common with data clustering and nearest neighbor search. ==Definition== An ''LSH family''〔 $\backslash mathcal\; F$ is defined for a metric space $\backslash mathcal\; M\; =(M,\; d)$, a threshold $R>0$ and an approximation factor $c>1$. This family $\backslash mathcal\; F$ is a family of functions $h:\backslash to\; S$ which map elements from the metric space to a bucket $s\; \backslash in\; S$. The LSH family satisfies the following conditions for any two points $p,\; q\; \backslash in$, using a function $h\; \backslash in\; \backslash mathcal\; F$ which is chosen uniformly at random: * if $d(p,q)\; \backslash le\; R$, then $h(p)=h(q)$ (i.e., and collide) with probability at least $P\_1$, * if $d(p,q)\; \backslash ge\; cR$, then $h(p)=h(q)$ with probability at most $P\_2$. A family is interesting when $P\_1>P\_2$. Such a family $\backslash mathcal\; F$ is called ''$(R,cR,P\_1,P\_2)$-sensitive''. Alternatively it is defined with respect to a universe of items that have a similarity function $\backslash phi\; :\; U\; \backslash times\; U\; \backslash to\; ()$. An LSH scheme is a family of hash functions coupled with a probability distribution over the functions such that a function $h\; \backslash in\; H$ chosen according to satisfies the property that $Pr\_\; (=\; h(b))\; =\; \backslash phi(a,b)$ for any $a,b\; \backslash in\; U$. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Locality-sensitive hashing」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.