In practice, the tensor product for 𝝍 can be directly formed if the dimensionality of 𝝍 can fit in the computer memory, i.e., if 𝑁 @ is small. An orthogonal set of coordinates 𝑽 can then directly be obtained from a matrix SVD of 𝝍. In cases where the dimensionality of 𝝍 overflows the computer memory, the same tensor-train decomposition as in AMUSEt can be applied, and the orthogonal coordinates 𝑽 can be computed by a higher-order SVD (𝝍 ; ≈ 𝝍, see step 2 of Scheme 2). A slight difference compared to the original formulation of AMUSEt is that, where within memory limitations, we apply the SVD to the full data tensor 𝝍 in order to leverage all available data points (𝝍 ; = 𝝍).