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Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
A solution to the (1+ ε)-approximate nearest neighbor search is a point or multiple points within distance (1+ ε) R from a query point, where R is the distance between the query point and its true nearest neighbor. [1] Reasons to approximate nearest neighbor search include the space and time costs of exact solutions in high-dimensional spaces ...
The Hierarchical navigable small world (HNSW) algorithm is a graph-based approximate nearest neighbor search technique used in many vector databases. [1] [2] Nearest neighbor search without an index involves computing the distance from the query to each point in the database, which for large datasets is computationally prohibitive.
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Best bin first is a search algorithm that is designed to efficiently find an approximate solution to the nearest neighbor search problem in very-high-dimensional spaces. The algorithm is based on a variant of the kd-tree search algorithm which makes indexing higher-dimensional spaces possible. Best bin first is an approximate algorithm which ...
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