Locality-sensitive hashing

Locality-sensitive hashing (LSH) is a technique to efficiently identify similar items in large datasets by grouping them into buckets. It uses special hash functions so that items that are alike are more likely to land in the same bucket, reducing the search space when looking for duplicates or similar data. This approach speeds up processes like image matching or document retrieval, by focusing only on items in the same or nearby buckets instead of comparing everything, making it a powerful tool for handling high-dimensional or complex data efficiently.