Kardi Teknomo Kardi Teknomo
Kardi Teknomo Kardi Teknomo Kardi Teknomo

Mahalanobis Distance


<Previous | Next | Content>

Mahalanobis distance is also called quadratic distance. It measures the separation of two groups of objects. Suppose we have two groups with means and , Mahalanobis distance is given by the following

Formula mahalanobis distance

The data of the two groups must have the same number of variables (the same number of columns) but not necessarily to have the same number of data (each group may have different number of rows).

In Matlab, the code is as follow:
function d=MahalanobisDistance(A, B)
% Return mahalanobis distance of two data matrices
% A and B (row = object, column = feature)
% @author: Kardi Teknomo
% http://people.revoledu.com/kardi/index.html

[n1, k1]=size(A);
[n2, k2]=size(B);
    disp('number of columns of A and B must be the same')
    xDiff=mean(A)-mean(B);       % mean diff row vector
    pC=n1/n*cA+n2/n*cB;           % pooled covariance matrix
    d=sqrt(xDiff*inv(pC)*xDiff');   % mahalanobis distance

The code above requires computation of Covariance matrix, which code is given below
function C=Covariance(X)
% Return covariance given data matrix X (row = object, column = feature)
% @author: Kardi Teknomo
% http://people.revoledu.com/kardi/index.html

Xc=X-repmat(mean(X),n,1);  % centered data
C=Xc'*Xc/n;                           % covariance

For example: Suppose we have two groups of data, each of group consists of two variables (x, y). The scattered plot of data is shown below.

data and mean scattered plot of data

First, we center the data on the arithmetic mean of each variable.

centered data for Mahalanobis distance

Covariance matrix of group Index of group for Mahalanobis distance is computed using centered data matrix centered data matrix

Covariance matrix for Mahalanobis distance

It produces covariance matrices for group 1 and 2 as follow

Covariance group 2 Covariance group 1

The pooled covariance matrix of the two groups is computed as weighted average of the covariance matrices. The weighted average takes this form

Mahalanobis distance.

The pooled covariance is computed using weighted average (10/15)*Covariance group 1 + (5/15)*Covariance group 2 yields

Pooled covariance matrix

The Mahalanobis distance is simply quadratic multiplication of mean difference and inverse of pooled covariance matrix.

Inverse pooled covariance for Mahalanobis distance Men differences for Mahalanobis distance

To perform the quadratic multiplication, check again the formula of Mahalanobis distance above. When you get mean difference, transpose it, and multiply it by inverse pooled covariance. After that, multiply the result with the mean difference again and you take the square root. The final result of Mahalanobis distance is

Mahalanobis distance

Spreadsheet example (MS Excel) of this Mahalanobis computation can be downloaded here.

Use the interactive program below to compute Mahalanobis distance. If you like this program, please recommend it to your friends.

How to use the program:

Input are two matrices name matrix A and matrix B that represent features coordinates of two objects. The columns indicate the features, and the rows are the observations. The number of features of the two objects must be equal (i.e. columns of matrix A = columns of matrix B). Each matrix should have at least 2 rows and 1 column.

    A matrix is sequence of numbers in a tabular format, inputted using the following format:
  • each number in a row is separated by a comma or a space
  • each row is separated by semicolon ;

Validate your input before running the program. The initial input values are the example.
Refresh your browser to get back the example.

This program is presented by © Kardi Teknomo

Input Matrix A
Input Matrix B

Samples of Applications of Mahalanobis Distance


<Previous | Next | Content>



Rate this tutorial


This tutorial is copyrighted.

Preferable reference for this tutorial is

Teknomo, Kardi (2015) Similarity Measurement. http:\people.revoledu.comkardi tutorialSimilarity

© 2015 Kardi Teknomo. All Rights Reserved.