Howtosignalprocessing
- Why Mahalanobis distance is better than Euclidean distance?
- What is the difference between Euclidean distance and Mahalanobis distance?
- Is a multivariate equivalent of the Euclidean?
- Why would we use Mahalanobis distance?
Why Mahalanobis distance is better than Euclidean distance?
Mahalanobis and Euclidean Distance
But, MD uses a covariance matrix unlike Euclidean. Because of that, MD works well when two or more variables are highly correlated and even if their scales are not the same . But, when two or more variables are not on the same scale, Euclidean distance results might misdirect.
What is the difference between Euclidean distance and Mahalanobis distance?
Mahalanobis distance is the scaled Euclidean distance when the covariance matrix is diagonal. In PCA the covariance matrix between components is diagonal. The scaled Euclidean distance is the Euclidean distance where the variables were scaled by their standard deviations.
Is a multivariate equivalent of the Euclidean?
Mahalonobis distance is the distance between a point and a distribution (as opposed to the distance between two points), making it the multivariate equivalent of the Euclidean distance.
Why would we use Mahalanobis distance?
The Mahalanobis distance is one of the most common measures in chemometrics, or indeed multivariate statistics. It can be used to determine whether a sample is an outlier, whether a process is in control or whether a sample is a member of a group or not.
