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 …

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 …

The Mahalanobis distance measures distance relative to the centroid — a base or central point which can be thought of as an overall mean for multivariate data.

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

This distance is based on the correlation between variables or the variance–covariance matrix. It differs from the Euclidean distance in that it takes into account the correlation of the data set …

Member of the Graduate Faculty Abhijit Mahalanobis is an associate professor of electrical and computer engineering at the University of Arizona. His primary research areas are video/image …

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 …

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 …

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 …

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.