ABSTRACT
Any method that operates on multidimensional data suffers from the curse of dimensionality, that is, complexity increases exponentially but the algorithms are not scalable. Such a problem arises in the tensor product (TP) model transformation as well when the model depends on many parameters. The size of the sampled model increases exponentially with respect to the number of parameters, and the higherorder singular value decomposition (HOSVD) cannot cope with the huge amount of data.
