How does Locality Sensitive Hashing (LSH) work?

LSH maps similar data points to the same 'buckets' with high probability, while dissimilar points are mapped to different buckets. It uses multiple hash functions. When searching for neighbors, the query vector is hashed, and only the vectors in the same buckets are compared. The more hash functions used, the higher the probability of finding true nearest neighbors, but also increasing computation. LSH is particularly effective in high-dimensional spaces, where traditional indexing methods struggle.