Mahalanobis Distance

By S. Islam. Encyclopedia of Biometrics, 2009

1,571 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f17bed70dc970b7d0edc3413ae737198b8e2b3b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, background): “Mahalanobis distance usually performs successfully with large data sets with reduced features, otherwise undesirable redundancies tend to distort the results [20].”
Multiple criteria construction management decisions considering relations between criteria (2010, methodology): “The authors suggest applying the Mahalanobis distance (Mahalanobis 1936; De Maesschalck et al. 2000; Schinka et al. 2003; McLachlan 1992) instead of Euclidean distance in TOPSIS algorithm to measure the distances of each alternative from the ideal solution and the negative-ideal solution and to…”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “Density-Based Methods OD methods include Gaussian distribution (Altman & Bland, 2005; Leys et al., 2013), Mahalanobis distance (De Maesschalck et al., 2000), Gaussian mixtures (Yang et al., 2009), and Local outlier factor (LOF) (Breunig et al., 2000).”
LATENT GROWTH MIXTURE MODELING: A SIMULATION STUDY (2007, background): “5, 1, 2, 3, 4 or 5, where SMD is the square root of Mahalanobis distance (McLahlan, 1999).”
Establishing collagen quality criteria for sulphur isotope analysis of archaeological bone collagen (2009, background): “The Mahalanobis distance as a robust measure for the difference (De Maesschalck et al. 2000) can detect statistic significant variance within a dataset with a number of outliers.”
OPERA models for predicting physicochemical properties and environmental fate endpoints (2018, methodology): “The leverage of a query chemical is proportional to its Mahalanobis distance measured from the centroid of the training set [119, 120].”
A comparison of linear approaches to filter out environmental effects in structural health monitoring (2018, methodology): “The principle of outlier analysis [7] is, for each sample of the multivariate feature vector yf , to compute the Mahalanobis squared-distance [17] given by, D(2)f 1⁄4 yf y f g T C 1⁄2 1 yf y f g ð3Þ Computing Df for all the data in the training set used to compute C 1⁄2 , it is possible to set a thr”
Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature (2014, background): “According to the characteristic of Mahalanobis distance [26], the target patterns are more likely to give larger values of δ(x(n)) since they are usually abnormal and have low probabilities of occurrence.”
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019, methodology): “To quantify the above condition, one can use Mahalanobis distance (De Maesschalck et al., 2000) to measure the distance of ( z z z t ,u u u t ) to distribution N (( z ∗ 2 ,t , u ∗ 2 ,t ) , δ 2 I ) , i.e., we want to check for the condition: for any arbitrary error tolerance (cid:15) (cid:48) > 0 .”
Crowd sensing of traffic anomalies based on human mobility and social media (2013, background): “In the following two sub-sections, we will discuss the two major steps, anomalous seed selection and graph expansion.”
Why We Should Stop Using the Kogut and Singh Index (2019, background): “…is singular or nearly singular and cannot be inverted; and because (c) the Euclidean distance can handle overdetermination (i.e., for the calculation of the variance–covariance matrix the number of objects in the data set has to be larger than the number of variables) (De Maesschalck et al. 2000).”
A Brief Review of Nearest Neighbor Algorithm for Learning and Classification (2019, background): “Mahalanobis is a new distance metric, the approach of which is covered in [8].”