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Copula mapping

    [W,V] = COPULAM(A,K)
    [W,V] = A*COPULAM
    C = B*W

 A Dataset, used for training the mapping
 B Dataset, same dimensionality as A, to be mapped
 K Desired output dimensionality, default dimensionality of A.

 W Trained mapping
 V Trained inverse mapping
 C Copula transformed dataset


The copula of a dataset transforms for every dimension every feature  value into the value of its cumulative feature density. So, A*W executes  the transform on the original data. A*W*V returns A again. This mapping  uses MAPSD in order to apply it on another dataset B of the same  dimensionality. It thereby creates an approximation trained by A.

If K is given, first a PCA to K dimensions is performed.

See also

datasets, mappings, pcam, mds, tsnem, mapsd,

PRTools Contents

PRTools User Guide

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