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NEAREST NEIGHBORS PROBLEM
"... DEFINITION Given a set of n points and a query point, q, the nearestneighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a twodimensional space with a query point, q ..."
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DEFINITION Given a set of n points and a query point, q, the nearestneighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a twodimensional space with a query point
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 459 (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
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
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Cited by 695 (14 self)
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We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin
Stereo Matching as a NearestNeighbor Problem
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We propose a representation of images, called intrinsic curves, that transforms stereo matching from a search problem into a nearestneighbor problem. Intrinsic curves are the paths that a set of local image descriptors trace as an image scanline is traversed from left to right. Intrinsic curves ar ..."
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Cited by 25 (2 self)
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We propose a representation of images, called intrinsic curves, that transforms stereo matching from a search problem into a nearestneighbor problem. Intrinsic curves are the paths that a set of local image descriptors trace as an image scanline is traversed from left to right. Intrinsic curves
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 455 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 522 (8 self)
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inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate
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 406 (2 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
Efficient algorithms for substring near neighbor problem
 in Proc. 17th Annu. ACMSIAM Sympos. Discrete Algorithms
"... In this paper we consider the problem of finding the approximate nearest neighbor when the data set points are the substrings of a given text T. Specifically, for a string T of length n, we present a data structure which does the following: given a pattern P, if there is a substring of T within the ..."
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Cited by 16 (3 self)
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In this paper we consider the problem of finding the approximate nearest neighbor when the data set points are the substrings of a given text T. Specifically, for a string T of length n, we present a data structure which does the following: given a pattern P, if there is a substring of T within
Notes on the Dynamic Bichromatic AllNearestNeighbors Problem
"... Given a set S of n points in the plane, each point having one of c colors, the bichromatic allnearestneighbors problem is the task to find (in the set S) a closest point of different color for each of the n points in S. We consider a dynamic variant of this problem where the points are fixed but ca ..."
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Given a set S of n points in the plane, each point having one of c colors, the bichromatic allnearestneighbors problem is the task to find (in the set S) a closest point of different color for each of the n points in S. We consider a dynamic variant of this problem where the points are fixed
A Simple Framework for the Generalized Nearest Neighbor Problem
"... The problem of finding a nearest neighbor from a set of points in R d to a complex query object has attracted considerable attention due to various applications in computational geometry, bioinformatics, information retrieval, etc. We propose a generic method that solves the problem for various cla ..."
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The problem of finding a nearest neighbor from a set of points in R d to a complex query object has attracted considerable attention due to various applications in computational geometry, bioinformatics, information retrieval, etc. We propose a generic method that solves the problem for various
Results 1  10
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