Mahalanobis distance

E753441

Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.

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Statements (46)

Predicate Object
instanceOf measure of distance
multivariate distance
statistical distance
accountsFor correlations between variables
appliedIn bioinformatics
chemometrics NERFINISHED
face recognition
finance
process monitoring
remote sensing
category Distance measures
Multivariate statistics
Statistical divergence and distance measures
compares point and distribution
two distributions
definedFor multivariate normal distribution
dependsOn inverse covariance matrix
field machine learning
multivariate analysis
outlier detection
pattern recognition
statistics
generalizes standardized distance
z-score
introducedBy Prasanta Chandra Mahalanobis NERFINISHED
introducedIn 1930s
invariantUnder affine transformations of data
is metric under suitable conditions
quadratic form
scale-invariant
unitless
namedAfter Prasanta Chandra Mahalanobis NERFINISHED
reducesTo Euclidean distance when covariance matrix is identity
relatedTo Euclidean distance
Gaussian discriminant analysis NERFINISHED
Hotelling's T-squared statistic NERFINISHED
requires positive definite covariance matrix
usedFor anomaly detection
classification
cluster analysis
discriminant analysis
fault detection
multivariate outlier detection
quality control
similarity measurement in feature space
uses covariance matrix

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Full triples — surface form annotated when it differs from this entity's canonical label.

Bhattacharyya distance relatedTo Mahalanobis distance