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15,560
Distance metric learning, with application to clustering with sideinformation,”
 in Advances in Neural Information Processing Systems 15,
, 2002
"... Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for ..."
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Cited by 818 (13 self)
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to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in Ê Ò , learns a distance metric over Ê Ò that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows
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
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 663 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
A comparison of string distance metrics for namematching tasks
, 2003
"... Using an opensource, Java toolkit of namematching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including editdistance metrics, fast heuristic string comparators, tok ..."
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Cited by 446 (11 self)
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Using an opensource, Java toolkit of namematching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including editdistance metrics, fast heuristic string comparators
Fastmap: A fast algorithm for indexing, datamining and visualization of traditional and multimedia datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several ..."
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Cited by 502 (22 self)
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A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several
PowerAware Routing in Mobile Ad Hoc Networks
, 1998
"... In this paper we present a case for using new poweraware metrics for determining routes in wireless ad hoc networks. We present five different metrics based on battery power consumption at nodes. We show that using these metrics in a shortestcost routing algorithm reduces the cost/packet of rout ..."
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Cited by 775 (5 self)
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property of using shortestcost routing is that packet delays do not increase. Finally, we note that our new metrics can be used in most traditional routing protocols for ad hoc networks. 1
Features of similarity.
 Psychological Review
, 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
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Cited by 1455 (2 self)
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. These models represent objects as points in some coordinate space such that the observed dissimilarities between objects correspond to the metric distances between the respective points. Practically all analyses of proximity data have been metric in nature, although some (e.g., hierarchical clustering) yield
GPSLess Low Cost Outdoor Localization for Very Small Devices.
 IEEE Personal Communications Magazine,
, 2000
"... AbstractInstrumenting 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 ..."
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Cited by 1000 (27 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
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 984 (32 self)
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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
Results 1  10
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15,560