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 first …

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; …

Mahalanobis distance is an effective multivariate distance metric that measures the distance between a point (vector) and a distribution. It has excellent applications in multivariate anomaly detection, …

May 14, 2025 · Explore comprehensive techniques to compute and interpret the Mahalanobis distance in multivariate analysis for reliable outlier detection.

4 days ago · The Multitasker: In the 1920s, Mahalanobis simultaneously served as a meteorologist, a physics professor in Presidency College, Calcutta, and the general secretary at Tagore’s University - …

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 was an Indian statistician. He developed several practical mathematical and statistical methods—including the Mahalanobis distance—that he later applied to India’s social and economic …

For example, data on a cross (isotropic covariance). This yields the local Mahalanobis distance, where for each point we compute neighbors using its local metric, defined using the local covariance matrix. …

Mahalanobis distance is a more sophisticated measure than Euclidean distance for identifying outliers. While Euclidean distance simply calculates the straight-line distance between a point and the center …

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.