| PRTools contents |
DISTMAHA
D = DISTMAHA (A,U,G)
| Input | |
| A | Dataset |
| U | Mean(s) (optional; default: estimate on classes in A) |
| G | Covariance(s) (optional; default: estimate on classes in A) |
| Output | |
| D | Mahalanobis distance matrix |
Computes the M*N Mahanalobis distance matrix of all vectors in M*K dataset A to an N*K dataset of points U, using the covariance matrix or matrices G. G should be either be one K*K matrix, or a K*K*N matrix containing a covariance matrix for each point in U.
When U and G are not specified, it estimates the C*C Mahalanobis distance matrix between all classes in A: the distance between the means, relative to the average per-class covariance matrix.
datasets, distm, proxm, meancov,
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