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1 Finding the Nearest Neighbors in Biological Databases Using Less Distance Computations
, 2008
"... Modern biological applications usually involve the similarity comparison between two objects, which is often computationally very expensive, such as whole genome pairwise alignment and protein three dimensional structure alignment. Nevertheless, being able to quickly identify the closest neighboring ..."
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, such that a significant number of the expensive distance computations can be avoided. The new method is able to dynamically locate virtual pivots, according to the query, with increasing pruning ability. Using the same or less amount of database preprocessing effort, the new method outperformed the second
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 high
Distance Browsing in Spatial Databases
, 1999
"... Two different techniques of browsing through a collection of spatial objects stored in an Rtree spatial data structure on the basis of their distances from an arbitrary spatial query object are compared. The conventional approach is one that makes use of a knearest neighbor algorithm where k is kn ..."
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Cited by 390 (20 self)
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neighbor algorithm for Rtrees [45]. Experiments show that the incremental nearest neighbor algorithm significantly outperforms the knearest neighbor algorithm for distance browsing queries in a spatial database that uses the Rtree as a spatial index. Moreover, the incremental nearest neighbor algorithm
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
Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules
, 2002
"... In a recent Physical Review Letters paper, Vicsek et. al. propose a simple but compelling discretetime model of n autonomous agents fi.e., points or particlesg all moving in the plane with the same speed but with dierent headings. Each agent's heading is updated using a local rule based on ..."
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Cited by 1245 (60 self)
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on the average of its own heading plus the headings of its \neighbors." In their paper, Vicsek et. al. provide simulation results which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized
Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces
, 1993
"... We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are highdim ..."
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Cited by 356 (5 self)
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We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are high
Discriminant Adaptive Nearest Neighbor Classification
, 1994
"... Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant an ..."
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Cited by 322 (1 self)
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Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant
Efficient similarity search in sequence databases
, 1994
"... We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Anot ..."
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Cited by 505 (21 self)
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. Another important observation is Parseval's theorem, which specifies that the Fourier transform preserves the Euclidean distance in the time or frequency domain. Having thus mapped sequences to a lowerdimensionality space by using only the first few Fourier coe cients, we use Rtrees to index
GPSless Low Cost Outdoor Localization For Very Small Devices
, 2000
"... Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, light, untethered and unobtrusive. The problem of localization, i.e., determining where a given no ..."
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Cited by 994 (29 self)
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in these networks. In this paper, we review localization techniques and evaluate the effectiveness of a very simple connectivitymetric method for localization in outdoor environments that makes use of the inherent radiofrequency (RF) communications capabilities of these devices. A fixed number of reference points
Predicting Internet Network Distance with CoordinatesBased Approaches
 In INFOCOM
, 2001
"... In this paper, we propose to use coordinatesbased mechanisms in a peertopeer architecture to predict Internet network distance (i.e. roundtrip propagation and transmission delay) . We study two mechanisms. The first is a previously proposed scheme, called the triangulated heuristic, which is bas ..."
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Cited by 633 (5 self)
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their own coordinates, these approaches allow end hosts to compute their interhost distances as soon as they discover each other. Moreover coordinates are very efficient in summarizing interhost distances, making these approaches very scalable. By performing experiments using measured Internet distance
Results 1  10
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