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A Statistical Approach to Algorithmic Analysis of HighDimensional NearestNeighbor Search
, 2004
"... A statistical approach to algorithmic analysis of highdimensional nearestneighbor search ..."
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A statistical approach to algorithmic analysis of highdimensional nearestneighbor search
High dimensional nearest neighbor searching
, 2006
"... As databases increasingly integrate different types of information such as timeseries, multimedia and scientific data, it becomes necessary to support efficient retrieval of multidimensional data. Both the dimensionality and the amount of data that needs to be processed are increasing rapidly. As ..."
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Cited by 3 (0 self)
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. VA þfile is especially useful for searching exact nearest neighbors (NN) in nonuniform high dimensional data sets. We then discuss how to improve the search and make it progressive by allowing some approximations in the query result. We develop a general framework for approximate NN queries
Multilevel Filtering for High Dimensional Nearest Neighbor Search
 In proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge
, 2000
"... Searching for nearest neighbors among a large number of vectors in a high dimensional space is usually costly and timeconsuming. To support such high dimensional nearest neighbor searches, low dimensional approximations of vectors are usually used to filter out many vectors (and hence to reduce the ..."
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Cited by 11 (1 self)
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Searching for nearest neighbors among a large number of vectors in a high dimensional space is usually costly and timeconsuming. To support such high dimensional nearest neighbor searches, low dimensional approximations of vectors are usually used to filter out many vectors (and hence to reduce
Quality and Efficiency in High Dimensional Nearest Neighbor Search
"... Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sublinearly with the dataset size, regardless of the data and query distributi ..."
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Cited by 30 (1 self)
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Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sublinearly with the dataset size, regardless of the data and query
Towards Meaningful HighDimensional Nearest Neighbor Search by HumanComputer Interaction
 In ICDE
, 2002
"... Nearest Neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may not b ..."
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Cited by 11 (2 self)
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Nearest Neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may
Lower bounds for high dimensional nearest neighbor search and related problems
, 1999
"... In spite of extensive and continuing research, for various geometric search problems (such as nearest neighbor search), the best algorithms known have performance that degrades exponentially in the dimension. This phenomenon is sometimes called the curse of dimensionality. Recent results [38, 37, 40 ..."
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Cited by 55 (2 self)
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In spite of extensive and continuing research, for various geometric search problems (such as nearest neighbor search), the best algorithms known have performance that degrades exponentially in the dimension. This phenomenon is sometimes called the curse of dimensionality. Recent results [38, 37
Adaptively Discovering Meaningful Patterns in HighDimensional Nearest Neighbor Search
, 2003
"... To query highdimensional databases, similarity search (or k nearest neighbor search) is the most extensively used method. However, since each attribute of high dimensional data records only contains very small amount of information, the distance of two highdimensional records may not always corr ..."
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To query highdimensional databases, similarity search (or k nearest neighbor search) is the most extensively used method. However, since each attribute of high dimensional data records only contains very small amount of information, the distance of two highdimensional records may not always
New Instability Results for High Dimensional Nearest Neighbor Search
, 906
"... Consider a dataset of n(d) points generated independently from Rd according to a common p.d.f. fd with support(fd) = [0, 1] d and sup{fd([0, 1] d)} growing subexponentially in d. We prove that: (i) if n(d) grows subexponentially in d, then, for any query point ⃗q d ∈ [0, 1] d and any ǫ> 0, the ..."
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bounded away from one. Moreover, we provide preliminary results along the lines of (i) when fd = N(⃗µd, Σd). Key words: information retrieval, curse of dimensionality 1.
The SRtree: An Index Structure for HighDimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform contentbased retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SStree had been proposed for ..."
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Cited by 442 (3 self)
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volume than bounding rectangles with highdimensional data and that this reduces search efficiency. To overcome this drawback, we propose a new index structure called the SRtree (Sphere/Rectangletree) which integrates bounding spheres and bounding rectangles. A region of the SRtree is specified
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 448 (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 highdimensional
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