International audienceWe derive a variance inequality on the squares of the pre-distances between n objects ensuring that the corresponding pre-distance matrix is Euclidean. The result is discussed in relation with some transformations making a pre-distance matrix Euclidean
In this paper the author extends Mathar\u27s result about the best Euclidean fit to a given distance...
In this paper, we provide an application-oriented characterization of a class of distance functions ...
AbstractIn this paper, we obtain a better estimate for the norm of inverses of Euclidean distance ma...
AbstractWe derive a variance inequality on the squares of the pre-distances between n objects ensuri...
AbstractIn a recent paper on the theory of Euclidean distance matrices, Gower derived an inequality ...
AbstractWe consider the problem of scaling a nondegenerate predistance matrix A to a doubly stochast...
AbstractMurthy and Sethi [M.N. Murthy, V.K. Sethi, Sankhya Ser. B 27 (1965) 201–210] gave a sharp up...
This book offers a comprehensive and accessible exposition of Euclidean Distance Matrices (EDMs) and...
In this paper we provide an application-oriented characterization of a class of distance measures mo...
<p>(a) Image A, (b) Image B, (c) Euclidean distance matrix between a<sub>11</sub> and b<sub>ij</sub>...
AbstractWe know that the cone of Euclidean distance matrices does not intersect the cone of positive...
International audienceIn this chapter, we focus on separable techniques for the Euclideanmetric and ...
The analysis of variance plays a fundamental role in statistical theory and practice, the standard E...
where a and b are twomultivariate observations, Σ− is the inverse of the variance-covariance matrix...
The Euclidean distance between two points P,Q of the Euclidean n-dimensional space En is a real valu...
In this paper the author extends Mathar\u27s result about the best Euclidean fit to a given distance...
In this paper, we provide an application-oriented characterization of a class of distance functions ...
AbstractIn this paper, we obtain a better estimate for the norm of inverses of Euclidean distance ma...
AbstractWe derive a variance inequality on the squares of the pre-distances between n objects ensuri...
AbstractIn a recent paper on the theory of Euclidean distance matrices, Gower derived an inequality ...
AbstractWe consider the problem of scaling a nondegenerate predistance matrix A to a doubly stochast...
AbstractMurthy and Sethi [M.N. Murthy, V.K. Sethi, Sankhya Ser. B 27 (1965) 201–210] gave a sharp up...
This book offers a comprehensive and accessible exposition of Euclidean Distance Matrices (EDMs) and...
In this paper we provide an application-oriented characterization of a class of distance measures mo...
<p>(a) Image A, (b) Image B, (c) Euclidean distance matrix between a<sub>11</sub> and b<sub>ij</sub>...
AbstractWe know that the cone of Euclidean distance matrices does not intersect the cone of positive...
International audienceIn this chapter, we focus on separable techniques for the Euclideanmetric and ...
The analysis of variance plays a fundamental role in statistical theory and practice, the standard E...
where a and b are twomultivariate observations, Σ− is the inverse of the variance-covariance matrix...
The Euclidean distance between two points P,Q of the Euclidean n-dimensional space En is a real valu...
In this paper the author extends Mathar\u27s result about the best Euclidean fit to a given distance...
In this paper, we provide an application-oriented characterization of a class of distance functions ...
AbstractIn this paper, we obtain a better estimate for the norm of inverses of Euclidean distance ma...
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