. . . . . . . . . . . . . . . . . . . . . . . . "Matrice de distance euclidienne"@fr . . . . . . . . "Euclidean distance matrix"@en . . "In mathematics, a Euclidean distance matrix is an n\u00D7n matrix representing the spacing of a set of n points in Euclidean space.For points in k-dimensional space \u211Dk, the elements of their Euclidean distance matrix A are given by squares of distances between them.That is where denotes the Euclidean norm on \u211Dk. In the context of (not necessarily Euclidean) distance matrices, the entries are usually defined directly as distances, not their squares.However, in the Euclidean case, squares of distances are used to avoid computing square roots and to simplify relevant theorems and algorithms. Euclidean distance matrices are closely related to Gram matrices (matrices of dot products, describing norms of vectors and angles between them).The latter are easily analyzed using methods of linear algebra.This allows to characterize Euclidean distance matrices and recover the points that realize it.A realization, if it exists, is unique up to rigid transformations, i.e. distance-preserving transformations of Euclidean space (rotations, reflections, translations). In practical applications, distances are noisy measurements or come from arbitrary dissimilarity estimates (not necessarily metric).The goal may be to visualize such data by points in Euclidean space whose distance matrix approximates a given dissimilarity matrix as well as possible \u2014 this is known as multidimensional scaling.Alternatively, given two sets of data already represented by points in Euclidean space, one may ask how similar they are in shape, that is, how closely can they be related by a distance-preserving transformation \u2014 this is Procrustes analysis.Some of the distances may also be missing or come unlabelled (as an unordered set or multiset instead of a matrix), leading to more complex algorithmic tasks, such as the graph realization problem or the turnpike problem (for points on a line)."@en . . . . . . . "En math\u00E9matiques, une matrice de distance euclidienne est une matrice de taille n \u00D7 n repr\u00E9sentant l'espacement d'un ensemble de points dans un espace euclidien. Si l'on note une matrice de distance euclidienne et des points sont d\u00E9finis dans un espace de dimension , alors les \u00E9l\u00E9ments de sont donn\u00E9s par o\u00F9 d\u00E9signe la norme euclidienne sur . Ainsi, la matrice des distances euclidienne sera de la forme :"@fr . . . . . . . . . . . . . . "17172"^^ . . "8092698"^^ . . . . . . . . . . "En math\u00E9matiques, une matrice de distance euclidienne est une matrice de taille n \u00D7 n repr\u00E9sentant l'espacement d'un ensemble de points dans un espace euclidien. Si l'on note une matrice de distance euclidienne et des points sont d\u00E9finis dans un espace de dimension , alors les \u00E9l\u00E9ments de sont donn\u00E9s par o\u00F9 d\u00E9signe la norme euclidienne sur . Ainsi, la matrice des distances euclidienne sera de la forme :"@fr . . . . . . . "1081738284"^^ . . . . . . . . . "In mathematics, a Euclidean distance matrix is an n\u00D7n matrix representing the spacing of a set of n points in Euclidean space.For points in k-dimensional space \u211Dk, the elements of their Euclidean distance matrix A are given by squares of distances between them.That is where denotes the Euclidean norm on \u211Dk. In the context of (not necessarily Euclidean) distance matrices, the entries are usually defined directly as distances, not their squares.However, in the Euclidean case, squares of distances are used to avoid computing square roots and to simplify relevant theorems and algorithms."@en . . . . . . . . . . . . . . .