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An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 983 (32 self)
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positive real ffl, a data point p is a (1 + ffl)approximate nearest neighbor of q if its distance from q is within a factor of (1 + ffl) of the distance to the true nearest neighbor. We show that it is possible to preprocess a set of n points in R d in O(dn log n) time and O(dn) space, so that given a
Nearoptimal hashing algorithms for approximate nearest neighbor in high dimensions
, 2008
"... In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The ..."
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Cited by 443 (7 self)
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In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query
Two Algorithms for NearestNeighbor Search in High Dimensions
, 1997
"... Representing data as points in a highdimensional space, so as to use geometric methods for indexing, is an algorithmic technique with a wide array of uses. It is central to a number of areas such as information retrieval, pattern recognition, and statistical data analysis; many of the problems aris ..."
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Cited by 201 (0 self)
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arising in these applications can involve several hundred or several thousand dimensions. We consider the nearestneighbor problem for ddimensional Euclidean space: we wish to preprocess a database of n points so that given a query point, one can efficiently determine its nearest neighbors
Nearest Neighbor Queries
, 1995
"... A frequently encountered type of query in Geographic Information Systems is to find the k nearest neighbor objects to a given point in space. Processing such queries requires substantially different search algorithms than those for location or range queries. In this paper we present an efficient bra ..."
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Cited by 594 (1 self)
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A frequently encountered type of query in Geographic Information Systems is to find the k nearest neighbor objects to a given point in space. Processing such queries requires substantially different search algorithms than those for location or range queries. In this paper we present an efficient
Range nearestneighbor query
 IEEE Transactions on Knowledge and Data Engineering (TKDE
"... A range nearestneighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearestneighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient inmemory ..."
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Cited by 40 (2 self)
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A range nearestneighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearestneighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient in
Quantitative analysis of nearestneighbors search in highdimensional samplingbased motion planning
 in Workshop on Algo. Found. of Robot
, 2006
"... Abstract: We quantitatively analyze the performance of exact and approximate nearestneighbors algorithms on increasingly highdimensional problems in the context of samplingbased motion planning. We study the impact of the dimension, number of samples, distance metrics, and sampling schemes on the ..."
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Cited by 21 (6 self)
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Abstract: We quantitatively analyze the performance of exact and approximate nearestneighbors algorithms on increasingly highdimensional problems in the context of samplingbased motion planning. We study the impact of the dimension, number of samples, distance metrics, and sampling schemes
When Is "Nearest Neighbor" Meaningful?
 In Int. Conf. on Database Theory
, 1999
"... . We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance ..."
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Cited by 402 (1 self)
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. We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches
Voting NearestNeighbor Subclassifiers
, 2000
"... Realistic applications of nearestneighbor classiers suer from capacityrelated problems. The size of today's data warehouses renders loading the entire data into the main memory impossible. Moreover, comparing each object with millions of stored examples can be prohibitively expensive. S ..."
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Cited by 7 (0 self)
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Realistic applications of nearestneighbor classiers suer from capacityrelated problems. The size of today's data warehouses renders loading the entire data into the main memory impossible. Moreover, comparing each object with millions of stored examples can be prohibitively expensive
An experimental comparison of the nearestneighbor and nearesthyperrectangle algorithms
 Machine Learning
, 1995
"... Abstract. Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest "generalized exemplar " (i.e., either a point or an axisparallel rectangle). They combine the distancebased character of nearest nei ..."
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Cited by 107 (4 self)
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neighbor (NN) classifiers with the axisparallel rectangle representation employed in many rulelearning systems. An implementation of NGE was compared to the knearest neighbor (kNN) algorithm in I 1 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were
Results 11  20
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1,292,145