Locality-sensitive hashing (LSH) is a technique used to hash data points in such a way that similar items are mapped to the same or nearby buckets with high probability. This method allows for efficient approximate nearest neighbor searches in high-dimensional spaces, making it particularly useful for large-scale data applications where traditional methods become impractical. By reducing the dimensionality of the data while preserving the locality, LSH enables faster data retrieval and enhances performance in various algorithms.
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