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manhattan distance

A collection of 3 posts

similarity measurement

Minkowski distance

Minkowski distance is a distance/ similarity measurement between two points in the normed vector space (N dimensional real space) and is a generalization of the Euclidean distance and the Manhattan distance. See the applications of Minkowshi distance and its visualization using an unit circle.

OpenGenus Foundation OpenGenus Foundation
similarity measurement

Euclidean vs Manhattan vs Chebyshev Distance

Euclidean distance, Manhattan distance and Chebyshev distance are all distance metrics which compute a number based on two data points. All the three metrics are useful in various use cases and differ in some important aspects such as computation and real life usage.

OpenGenus Foundation OpenGenus Foundation
similarity measurement

Manhattan distance (L1 norm)

Manhattan distance (L1 norm) is a distance metric between two points in a N dimensional vector space. It is the sum of the lengths of the projections of the line segment between the points onto the coordinate axes. It was introduced by Hermann Minkowski. It is used in regression analysis

OpenGenus Foundation OpenGenus Foundation
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