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  2. Euclidean distance matrix - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance_matrix

    In mathematics, a Euclidean distance matrix is an n×n matrix representing the spacing of a set of n points in Euclidean space. For points x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} in k -dimensional space ℝ k , the elements of their Euclidean distance matrix A are given by squares of distances between them.

  3. Distance matrix - Wikipedia

    en.wikipedia.org/wiki/Distance_matrix

    In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]

  4. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    In mathematics, the Euclidean distance between two points in Euclidean space is the length of the line segment between them. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem , and therefore is occasionally called the Pythagorean distance .

  5. Dot product - Wikipedia

    en.wikipedia.org/wiki/Dot_product

    In mathematics, the dot product or scalar product [note 1] is an algebraic operation that takes two equal-length sequences of numbers (usually coordinate vectors), and returns a single number. In Euclidean geometry, the dot product of the Cartesian coordinates of two vectors is widely used.

  6. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    The normalized angle, referred to as angular distance, between any two vectors and is a formal distance metric and can be calculated from the cosine similarity. [5] The complement of the angular distance metric can then be used to define angular similarity function bounded between 0 and 1, inclusive.

  7. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    Suppose a vector norm ‖ ‖ on and a vector norm ‖ ‖ on are given. Any matrix A induces a linear operator from to with respect to the standard basis, and one defines the corresponding induced norm or operator norm or subordinate norm on the space of all matrices as follows: ‖ ‖, = {‖ ‖: ‖ ‖ =} = {‖ ‖ ‖ ‖:} . where denotes the supremum.

  8. List of named matrices - Wikipedia

    en.wikipedia.org/wiki/List_of_named_matrices

    Euclidean distance matrix is a special case Euclidean distance matrix: A matrix that describes the pairwise distances between points in Euclidean space: See also distance matrix: Fundamental matrix: The matrix formed from the fundamental solutions of a system of linear differential equations: Generator matrix: In Coding theory, a matrix whose ...

  9. Metric space - Wikipedia

    en.wikipedia.org/wiki/Metric_space

    Wasserstein metrics measure the distance between two measures on the same metric space. The Wasserstein distance between two measures is, roughly speaking, the cost of transporting one to the other. The set of all m by n matrices over some field is a metric space with respect to the rank distance (,) = ().