The Mahalanobis distance is a measure of the distance between a point and a probability distribution , introduced by P. C. Mahalanobis in 1936. [1] The mathematical details of Mahalanobis distance …

The Mahalanobis distance (MD) is the distance between two points in multivariate space. In a regular Euclidean space, variables (e.g. x, y, z) are represented by axes drawn at right angles to each other; …

May 14, 2025 · The Mahalanobis distance is a robust multivariate distance metric that transcends the limitations of the Euclidean distance by accounting for variable correlations and variations.

In this post, we covered nearly everything about Mahalanobis distance: the intuition behind the formula, the actual calculation in python and how it can be used for multivariate anomaly detection, binary …

Mahalanobis distance is a multivariate distance metric that measures how far a point is from the center of a distribution, taking into account correlations between variables.

P.C. Mahalanobis, Indian statistician who devised the Mahalanobis distance and was instrumental in formulating India’s strategy for industrialization in the Second Five-Year Plan (1956–61).

This yields the local Mahalanobis distance, where for each point we compute neighbors using its local metric, defined using the local covariance matrix. This can be used to design an iterated kNN …

In particular, this is the correct formula for the Mahalanobis distance in the original coordinates. The amounts by which the axes are expanded in the last step are the (square roots of the) eigenvalues of …

We classify a feature vector x by measuring the Mahalanobis distance from x to each of the means, and assigning x to the class for which the Mahalanobis distance is minimum.

What Is Mahalanobis Distance? Mahalanobis distance is a statistical measure used to determine the similarity between two data points in a multidimensional space. It is instrumental in data analysis, …