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Informationtheoretic metric learning
 in NIPS 2006 Workshop on Learning to Compare Examples
, 2007
"... We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem. Spe ..."
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Cited by 359 (15 self)
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We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem
Efficient Retrieval of Similar Time Sequences Under Time Warping
, 1997
"... Fast similarity searching in large timesequence databases has attracted a lot of research interest [1, 5, 2, 6, 3, 10]. All of them use the Euclidean distance (L 2), or some variation of L ..."
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Cited by 215 (5 self)
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Fast similarity searching in large timesequence databases has attracted a lot of research interest [1, 5, 2, 6, 3, 10]. All of them use the Euclidean distance (L 2), or some variation of L
Svmknn: Discriminative nearest neighbor classification for visual category recognition
 in CVPR
, 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
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Cited by 342 (10 self)
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nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in biasvariance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve timeconsuming optimization and computation of pairwise distances
Derivative dynamic time warping
 In SIAM International Conference on Data Mining
, 2001
"... Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two se ..."
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Cited by 121 (1 self)
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two
Logistic Regression, AdaBoost and Bregman Distances
, 2000
"... We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt al ..."
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Cited by 259 (45 self)
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We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt
Distance transforms of sampled functions
 Cornell Computing and Information Science
, 2004
"... This paper provides lineartime algorithms for solving a class of minimization problems involving a cost function with both local and spatial terms. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary ..."
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Cited by 175 (9 self)
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This paper provides lineartime algorithms for solving a class of minimization problems involving a cost function with both local and spatial terms. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced
Graph evolution: Densification and shrinking diameters
 ACM TKDD
, 2007
"... How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include hea ..."
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Cited by 267 (16 self)
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, and we observe some surprising phenomena. First, most of these graphs densify over time, with the number of edges growing superlinearly in the number of nodes. Second, the average distance between nodes often shrinks over time, in contrast to the conventional wisdom that such distance parameters should
Optimal mass transport for registration and warping
 International Journal on Computer Vision
, 2004
"... Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass tran ..."
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Cited by 58 (9 self)
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Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass
Indexing multidimensional timeseries with support for multiple distance measures
, 2003
"... Although most timeseries data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of ..."
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Cited by 121 (15 self)
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Although most timeseries data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis
Scaling up Dynamic Time Warping for Datamining Applications
 In Proc. 6th Int. Conf. on Knowledge Discovery and Data Mining
, 2000
"... There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle dist ..."
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Cited by 84 (3 self)
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distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate
Results 11  20
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5,895