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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 nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem approximately. In that problem, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. The algorithm quickly yields a short tour, but usually not the optimal one.
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.
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 ...
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.
Proximity problems is a class of problems in computational geometry which involve estimation of distances between geometric objects. A subset of these problems stated in terms of points only are sometimes referred to as closest point problems , [ 1 ] although the term "closest point problem" is also used synonymously to the nearest neighbor ...
In probability and statistics, a nearest neighbor function, nearest neighbor distance distribution, [1] nearest-neighbor distribution function [2] or nearest neighbor distribution [3] is a mathematical function that is defined in relation to mathematical objects known as point processes, which are often used as mathematical models of physical phenomena representable as randomly positioned ...
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.
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