lsh, Nearest neighbor search in high dimensions
نویسنده
چکیده
Calculating distance pairs is O(n2) in memory and time and finding the nearest neighbor is O(n) in time. Tree indexing techniques like kd-tree [2] were developed to cope with large n, however their performance quickly breaks down for p > 3 [3]. Locality sensitive hashing (LSH) [3] is a technique for generating hash numbers from high dimensional data, such that nearby points have identical hashes. This enables efficient nearest neighbor search for (very) high dimensional data sets. It has been successfully applied to several problems including text similarity search [5].
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