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An experimental study on combining euclidean distances
 in The 2nd International Workshop on Cognitive Information Processing
"... Abstract—Combining different distance matrices or dissimilarity representations usually can increase the performance of individual ones. In this work, we experimentally study on the performance of combining Euclidean distances and its relationship with the nonEuclideaness produced from combining ..."
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Abstract—Combining different distance matrices or dissimilarity representations usually can increase the performance of individual ones. In this work, we experimentally study on the performance of combining Euclidean distances and its relationship with the nonEuclideaness produced from combining
On the Nonnegative Rank of Euclidean Distance Matrices
"... The Euclidean distance matrix for n distinct points in R r is generically of rank r + 2. It is shown in this paper via a geometric argument that its nonnegative rank for the case r = 1 is generically n. Key words: Euclidean distance matrix, nonnegative rank factorization, nonnegative rank 1. ..."
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The Euclidean distance matrix for n distinct points in R r is generically of rank r + 2. It is shown in this paper via a geometric argument that its nonnegative rank for the case r = 1 is generically n. Key words: Euclidean distance matrix, nonnegative rank factorization, nonnegative rank 1.
The Euclidean distance degree of an algebraic variety
, 2013
"... The nearest point map of a real algebraic variety with respect to Euclidean distance is an algebraic function. For instance, for varieties of low rank matrices, the EckartYoung Theorem states that this map is given by the singular value decomposition. This article develops a theory of such nearest ..."
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Cited by 14 (2 self)
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The nearest point map of a real algebraic variety with respect to Euclidean distance is an algebraic function. For instance, for varieties of low rank matrices, the EckartYoung Theorem states that this map is given by the singular value decomposition. This article develops a theory of such nearest
is the Euclidean distance from
"... L [2]. We call this the Waxman 2 model. ffl Scaling P (u; v) by a factor kffl=n, where ffl is the desired average node degree, n is the number of nodes and k is a constant that depends on ff and fi [1]. ffl Allowing ff ? 1:0 [3]. The second model, while not fundamentally different than the Waxman ..."
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L [2]. We call this the Waxman 2 model. ffl Scaling P (u; v) by a factor kffl=n, where ffl is the desired average node degree, n is the number of nodes and k is a constant that depends on ff and fi [1]. ffl Allowing ff ? 1:0 [3]. The second model, while not fundamentally different than the Waxman model, is interesting because the addition of the factor radius = kffl gives more direct control over the number of edges in the graphs that are generated, provided k is known. Clearly the ff parameter of the Waxman model can be chosen to be equivalent to any particular setting of the parameters k; ffl; n and ff in the DoarLeslie model. We also propose two new models, intended to relate edge
Region growing Euclidean distance transforms
 97, Lecture Notes in Computer Science
, 1997
"... By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to detect potentially erroneous pixels and correct them by using larger neighbourhoods, w ..."
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Cited by 19 (5 self)
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By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to detect potentially erroneous pixels and correct them by using larger neighbourhoods
Isomap Based on the Image Euclidean Distance
"... Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (Isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold ..."
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Cited by 1 (0 self)
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learning are corrupted with noises, the Isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images—the Image Euclidean distance based Isomap (ImIsomap), in which we use a new distance for images called IMage Euclidean Distance
Region Growing Euclidean Distance Transforms
 97, Lecture Notes in Computer Science
, 1997
"... : By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to detect potentially erroneous pixels and correct them by using larger neighbourho ..."
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: By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to detect potentially erroneous pixels and correct them by using larger
Approximation of Euclidean distances by chamfer distances
"... Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon ”small ” neighborhoods still outperforms it e.g. in terms of computation time ..."
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Cited by 2 (0 self)
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Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon ”small ” neighborhoods still outperforms it e.g. in terms of computation
Results 11  20
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4,774