Minkowski distance

The Minkowski distance or Minkowski metric is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance. It is named after the mathematician Hermann Minkowski.

Definition

The Minkowski distance of order between two points
is defined as:
For the Minkowski distance is a metric as a result of the Minkowski inequality. When the distance between and is but the point is at a distance from both of these points. Since this violates the triangle inequality, for it is not a metric. However, a metric can be obtained for these values by simply removing the exponent of The resulting metric is also an F-norm.
Minkowski distance is typically used with being 1 or 2, which correspond to the Manhattan distance and the Euclidean distance, respectively. In the limiting case of reaching infinity, we obtain the Chebyshev distance:
Similarly, for reaching negative infinity, we have:
The Minkowski distance can also be viewed as a multiple of the power mean of the component-wise differences between and
The following figure shows unit circles with various values of :

Applications

The Minkowski metric is very useful in the field of machine learning and AI. Many popular machine learning algorithms use specific distance metrics such as the aforementioned to compare the similarity of two data points. Depending on the nature of the data being analyzed, various metrics can be used. The Minkowski metric is most useful for numerical datasets where one wants to determine the similarity of size between multiple datapoint vectors.