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Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... 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 the us ..."
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Cited by 799 (14 self)
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examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in R , learns a distance metric over R that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us
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 685 (15 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
Discrete approximations of nonmetrical distances
 FOURTH HUNGARIAN CONFERENCE ON COMPUTER GRAPHICS AND GEOMETRY, BUDAPEST
, 2007
"... In this paper we will introduce a new method for approximating nonmetrical Minkowski distances. The existing approaches for Minkowski metrics considering distance functions based on local neighborhoods are not suitable for this task in their present form. In our approach we can overcome this diffic ..."
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In this paper we will introduce a new method for approximating nonmetrical Minkowski distances. The existing approaches for Minkowski metrics considering distance functions based on local neighborhoods are not suitable for this task in their present form. In our approach we can overcome
The earth mover’s distance as a metric for image retrieval
 International Journal of Computer Vision
, 2000
"... 1 Introduction Multidimensional distributions are often used in computer vision to describe and summarize different features of an image. For example, the onedimensional distribution of image intensities describes the overall brightness content of a grayscale image, and a threedimensional distrib ..."
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Cited by 706 (5 self)
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1 Introduction Multidimensional distributions are often used in computer vision to describe and summarize different features of an image. For example, the onedimensional distribution of image intensities describes the overall brightness content of a grayscale image, and a threedimensional distribution can play a similar role for color images. The texture content of an image can be described by a distribution of local signal energy over frequency. These descriptors can be used in a variety of applications including, for example, image retrieval.
Clustering for Metric and NonMetric Distance Measures
, 2009
"... We study a generalization of the kmedian problem with respect to an arbitrary dissimilarity measure D. Given a finite set P of size n, our goal is to find a set C of size k such that the sum of errors D(P, C) = ∑ D(p, c) is minimized. The main result in this paper can be p∈P minc∈C stated as follo ..."
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Cited by 8 (1 self)
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(mk/ɛ)) , where m is a constant that depends only on ɛ and D. Using this characterization, we obtain the first linear time (1 + ɛ)approximation algorithms for the kmedian problem in an arbitrary metric space with bounded doubling dimension, for the KullbackLeibler divergence (relative entropy), for the Itakura
A Metrics Suite for Object Oriented Design
 IEEE Trans. Softw. Eng
, 1994
"... Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to softwar ..."
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Cited by 1079 (3 self)
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to software development, with perhaps the most prominent being objectorientation (00). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a
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 652 (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
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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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
Adhoc OnDemand Distance Vector Routing
 IN PROCEEDINGS OF THE 2ND IEEE WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS
, 1997
"... An adhoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. In this paper we present Adhoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such adhoc n ..."
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Cited by 3167 (15 self)
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An adhoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. In this paper we present Adhoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such ad
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