LSH, an acronym for Locality Sensitive Hashing, is a technique used for approximate nearest neighbor search in high-dimensional spaces. It is a method specifically designed for dealing with big data sets to efficiently find approximate nearest neighbors, rather than exact matches, based on a given similarity measure or distance function.
LSH works by randomly partitioning the data points into multiple buckets, such that similar points have a high probability of landing in the same or nearby buckets. By doing so, LSH reduces the search space and allows for efficient similarity search operations.
The process starts with the generation of a set of random hash functions for each bucket. Then, for each data point, the hash functions are applied, and the points are assigned to their corresponding buckets. When performing a similarity search, LSH only considers the buckets that are likely to contain similar points, drastically reducing the search space and improving performance.
The trade-off of using LSH is that it may introduce some false positives and negatives due to the approximate nature of the search. However, LSH is particularly useful when dealing with high-dimensional data, as traditional indexing methods tend to struggle in this scenario.
Overall, LSH is a powerful technique for approximate nearest neighbor search, enabling efficient similarity searches in large datasets by leveraging random bucket partitioning and random hashing functions.
