Locality Sensitive Hashing
conceptAlgorithm
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Overview
Founded1998
Use caseapproximate nearest neighbor search in high-dimensional spaces
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Last updatedUpdated 5 days ago
WikidataQ1641203
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100% unverified
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Locality Sensitive Hashing

concept

Algorithmic technique that hashes similar input items into the same buckets with high probability.

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developed by

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Piotr Indyk and Rajeev MotwaniUnverifiedHighFresh1

primary use case

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approximate nearest neighbor search in high-dimensional spacesUnverifiedHighFresh1
approximate nearest neighbor searchUnverifiedHighFresh1
similarity search in large datasetsUnverifiedHighFresh1
dimensionality reduction for high-dimensional dataUnverifiedHighFresh1
similarity search and clustering of documentsUnverifiedModerateFresh1
duplicate detection in large datasetsUnverifiedModerateFresh1
deduplication and clusteringUnverifiedModerateFresh1
recommendation systems and information retrievalUnverifiedModerateFresh1

founded year

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1998UnverifiedHighFresh1

alternative to

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exhaustive k-nearest neighbors searchUnverifiedHighFresh1
brute force nearest neighbor searchUnverifiedHighFresh1
exact nearest neighbor searchUnverifiedHighFresh1
k-d treesUnverifiedModerateFresh1
tree-based nearest neighbor methodsUnverifiedModerateFresh1

supports protocol

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Hamming distanceUnverifiedHighFresh1
Jaccard similarityUnverifiedHighFresh1
cosine similarityUnverifiedModerateFresh1

supports model

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MinHash for Jaccard similarityUnverifiedHighFresh1
random projection for Euclidean distanceUnverifiedModerateFresh1

based on

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probabilistic hashing techniquesUnverifiedHighFresh1
probabilistic dimension reduction techniquesUnverifiedModerateFresh1

implemented by

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Apache Spark MLlibUnverifiedHighFresh1
scikit-learnUnverifiedModerateFresh1

integrates with

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machine learning frameworksUnverifiedModerateFresh1
Apache Spark MLlibUnverifiedModerateFresh1

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Claim count: 36Last updated: 4/5/2026Edit history