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

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.

Named after Indian statistician Prasanta Chandra Mahalanobis, the method computes how far a point is from the mean of the distribution — not in terms of raw distance, but in standard …

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.

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 …