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

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.

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 …

Oct 7, 2025 · Mahalanobis distance is a measure used in multivariate analysis to determine the distance between a point and a distribution. It is particularly useful in identifying outliers and in …

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, pattern recognition, and …

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 …