Results 1  10
of
435,173
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 ..."
Abstract

Cited by 801 (13 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 682 (14 self)
 Add to MetaCart
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 ..."
Abstract
 Add to MetaCart
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
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 ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
(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
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 ..."
Abstract

Cited by 650 (37 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 432 (11 self)
 Add to MetaCart
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
Class Representation and Image Retrieval with NonMetric Distances
, 1998
"... One of the key problems in appearancebased vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performance, or that use robust image matching methods, often make use of similarity judgments that are nonmetric; but when the ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
representatives of a class that accurately characterize it. We show that existing condensing techniques for finding class representatives are illsuited to deal with nonmetric dataspaces. We then focus on developing techniques for solving this problem, emphasizing two points: First, we show that the distance
ParameterFree Determination of Distance Thresholds for Metric Distance Constraints
"... Abstract—The importance of introducing distance constraints to data dependencies, such as differential dependencies (DDs) [28], has recently been recognized. The metric distance constraints are tolerant to small variations, which enable them apply to wide data quality checking applications, such as ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Abstract—The importance of introducing distance constraints to data dependencies, such as differential dependencies (DDs) [28], has recently been recognized. The metric distance constraints are tolerant to small variations, which enable them apply to wide data quality checking applications
Results 1  10
of
435,173