1. Multivariate Distance and Similarity Robert F. Murphy Cytometry Development Workshop 2000

2. General Multivariate Dataset u We are given values of p variables for n independent observations u Construct an n x p matrix M consisting of vectors X 1 through X n each of length p

3. Multivariate Sample Mean u Define mean vector I of length p or matrix notation vector notation

4. Multivariate Variance  Define variance vector   of length p matrix notation

5. Multivariate Variance u or vector notation

6. Covariance Matrix  Define a p x p matrix cov (called the covariance matrix) analogous to  2

7. Covariance Matrix u Note that the covariance of a variable with itself is simply the variance of that variable

8. Univariate Distance u The simple distance between the values of a single variable j for two observations i and l is

9. Univariate z-score Distance u To measure distance in units of standard deviation between the values of a single variable j for two observations i and l we define the z-score distance

10. Bivariate Euclidean Distance u The most commonly used measure of distance between two observations i and l on two variables j and k is the Euclidean distance

11. Multivariate Euclidean Distance u This can be extended to more than two variables

12. Effects of variance and covariance on Euclidean distance Points A and B have similar Euclidean distances from the mean, but point B is clearly “more different” from the population than point A. B A The ellipse shows the 50% contour of a hypothetical population.

13. Mahalanobis Distance u To account for differences in variance between the variables, and to account for correlations between variables, we use the Mahalanobis distance