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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Mahalanobis distance canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T8728984 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mahalanobis distance Context triple: [Bhattacharyya distance, relatedTo, Mahalanobis distance]
-
A.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
B.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
C.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
D.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
E.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mahalanobis distance Target entity description: 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.
-
A.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
B.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
C.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
D.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
E.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
measure of distance
ⓘ
multivariate distance ⓘ statistical distance ⓘ |
| accountsFor | correlations between variables ⓘ |
| appliedIn |
bioinformatics
ⓘ
chemometrics ⓘ 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 ⓘ |
| introducedIn | 1930s ⓘ |
| invariantUnder | affine transformations of data ⓘ |
| is |
metric under suitable conditions
ⓘ
quadratic form ⓘ scale-invariant ⓘ unitless ⓘ |
| namedAfter | Prasanta Chandra Mahalanobis ⓘ |
| reducesTo | Euclidean distance when covariance matrix is identity ⓘ |
| relatedTo |
Euclidean distance
ⓘ
Gaussian discriminant analysis ⓘ Hotelling's T-squared statistic ⓘ
linked to:
Hotelling’s T-squared distribution
|
| 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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Mahalanobis distance Description of subject: 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.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.