Compute a dissimilarity matrix (DSM) using cross-validation.
The distance computation is performed from the average of all-but-one “training” folds to the remaining “test” fold. This is repeated with each fold acting as the “test” fold once. The resulting distances are averaged and the result used in the final DSM.
For each item, all the features. The first dimension are the folds used for cross-validation, items are along the second dimension, and all other dimensions will be flattened and treated as features.
The distance metric to use to compute the DSM. Can be any metric
scipy.spatial.distance.pdist(). When a function is
specified, it needs to take in two vectors and output a single number.
See also the
dist_params parameter to specify and additional
parameter for the distance function.
Defaults to ‘correlation’.
Extra arguments for the distance metric. Refer to
scipy.spatial.distance for a list of all other metrics and their
The cross-validated DSM, in condensed form.