Algorithmic technique for approximate nearest neighbor search in high-dimensional spaces using hash functions to map similar items to the same buckets with high probability.
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Locality-Sensitive Hashing (LSH) is an algorithmic technique that hashes similar input items into the same "buckets" with high probability (the number of buckets being much smaller than the universe of possible input items).
LSH uses hash functions that map similar items to the same hash bucket with high probability, unlike traditional hash functions that try to avoid collisions. This property makes it particularly useful for approximate nearest neighbor search in high-dimensional spaces.
java-LSH - Implements LSH as described in Leskovec, Rajaraman & Ullman (2014), "Mining of Massive Datasets"
LSHKIT - A C++ Locality Sensitive Hashing Library
For production use, libraries built for similarity search like FAISS or managed solutions like Pinecone are recommended instead of basic LSH implementations.
Various open-source implementations available.