  Multivariate polynomials arise in many different disciplines. Representing
such a polynomial as a vector of univariate polynomials can offer useful
insight, as well as more intuitive understanding. For this, techniques based on
tensor methods are known, but these have only been studied in the exact case.
In this paper, we generalize an existing method to the noisy case, by
introducing a weight factor in the tensor decomposition. Finally, we apply the
proposed weighted decoupling algorithm in the domain of system identification,
and observe smaller model errors.
