3. The method of claim 2 , further comprising: determining, by the computing device, a subspace of an eigenvector representation of the correlation representation, wherein the subspace includes given eigenvectors representative of given variances greater than variances represented by other eigenvectors in the eigenvector representation; and determining, based on the subspace, local weights for the individual costs, wherein the given individual cost is weighted based on a given local weight of the local weights.
3. The method of claim 2 , further comprising: determining, by the computing device, a subspace of an eigenvector representation of the correlation representation, wherein the subspace includes given eigenvectors representative of given variances greater than variances represented by other eigenvectors in the eigenvector representation; and determining, based on the subspace, local weights for the individual costs, wherein the given individual cost is weighted based on a given local weight of the local weights. 5. The method of claim 3 , wherein the subspace is configured to include a given quantity of the given eigenvectors.