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 …

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.

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 …

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).

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.

Mahalanobis distance measures the distance of a point x from a data distribution. The data distribution is characterized by a mean and the covariance matrix, thus is hypothesized as a …

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 …

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) …

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.