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Reconstruction of Integers from Pairwise Distances
"... Given a set of integers, one can easily construct the set of their pairwise distances. We consider the inverse problem: given a set of pairwise distances, find the integer set which realizes the pairwise distance set. This problem arises in a lot of fields in engineering and applied physics, and has ..."
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Given a set of integers, one can easily construct the set of their pairwise distances. We consider the inverse problem: given a set of pairwise distances, find the integer set which realizes the pairwise distance set. This problem arises in a lot of fields in engineering and applied physics
Designing Networks with Bounded Pairwise Distance
"... We study the following network design problem: Given a communication network, find a minimum cost subset of missing links such that adding these links to the network makes every pair of points within distance at most d from each other. Theproblemhasbeenstudied earlier[17]undertheassumptionthatallli ..."
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Cited by 56 (0 self)
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We study the following network design problem: Given a communication network, find a minimum cost subset of missing links such that adding these links to the network makes every pair of points within distance at most d from each other. Theproblemhasbeenstudied earlier[17
Learning Texture Similarity with Perceptual Pairwise Distance
, 2005
"... In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning si ..."
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In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning
Beyond pairwise distances: Neighborjoining with phylogenetic diversity estimates
 J. MOL BIOL EVOL
, 2006
"... The NeighborJoining algorithm is a recursive procedure for reconstructing trees that is based on a transformation of pairwise distances between leaves. We present a generalization of the neighborjoining transformation, which uses estimates of phylogenetic diversity rather than pairwise distances ..."
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Cited by 13 (1 self)
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The NeighborJoining algorithm is a recursive procedure for reconstructing trees that is based on a transformation of pairwise distances between leaves. We present a generalization of the neighborjoining transformation, which uses estimates of phylogenetic diversity rather than pairwise
Fast Neighborhood Subgraph Pairwise Distance Kernel
"... We introduce a novel graph kernel called the Neighborhood Subgraph Pairwise Distance Kernel. The kernel decomposes a graph into all pairs of neighborhood subgraphs of small radius at increasing distances. We show that using a fast graph invariant we obtain significant speedups in the Gram matrix co ..."
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Cited by 24 (10 self)
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We introduce a novel graph kernel called the Neighborhood Subgraph Pairwise Distance Kernel. The kernel decomposes a graph into all pairs of neighborhood subgraphs of small radius at increasing distances. We show that using a fast graph invariant we obtain significant speedups in the Gram matrix
Indirect Radio Interferometric Localization via Pairwise Distances
"... The Radio Interferometric Positioning System (RIPS), introduced by Maroti et. al. [1], provides a means for very accurate sensor localization with very minimal device hardware requirements. To avoid stopping in a significant number of locally optimal location solutions, RIPS employs a genetic optimi ..."
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Cited by 7 (0 self)
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optimization localization algorithm. This paper proposes an indirect localization algorithm which first estimates pairwise distances, and then uses them to estimate coordinates via distributed weighted multidimensional scaling (dwMDS). The pairwise distances are iteratively improved and coordinates re
Which Point Configurations are Determined by the Distribution of their Pairwise Distances?
, 2006
"... In a previous paper we showed that, for any n> = m + 2, most sets of n points in Rm are determined(up to rotations, reflections, translations and relabeling of the points) by the distribution of their pairwise distances. But there are some exceptional point configurations which are not reconstr ..."
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Cited by 12 (1 self)
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In a previous paper we showed that, for any n> = m + 2, most sets of n points in Rm are determined(up to rotations, reflections, translations and relabeling of the points) by the distribution of their pairwise distances. But there are some exceptional point configurations which
Hypothesis testing using pairwise distances and associated kernels
 in Proc. International Conference on Machine Learning ICML
, 2012
"... We provide a unifying framework linking two classes of statistics used in twosample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spac ..."
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Cited by 10 (6 self)
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We provide a unifying framework linking two classes of statistics used in twosample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert
2.2. Pairwise Distance Linear Regression............................... 338
"... Abstract...................................................................... 332 ..."
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Abstract...................................................................... 332
A Novel Spectral Clustering Method Based on Pairwise Distance Matrix
"... In general, the similarity measure is indispensable for most traditional spectral clustering algorithms since these algorithms typically begin with the pairwise similarity matrix of a given dataset. However, a general type of input for most clustering applications is the pairwise distance matrix. In ..."
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Cited by 1 (0 self)
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In general, the similarity measure is indispensable for most traditional spectral clustering algorithms since these algorithms typically begin with the pairwise similarity matrix of a given dataset. However, a general type of input for most clustering applications is the pairwise distance matrix
Results 1  10
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