Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as ll as more intuitive understanding. For this, techniques based on tensor methods are known, but these have only been studied in the exact case. ,  generalize an existing method to the noisy case, by introducing a ight factor in the tensor decomposition. Finally,  apply the proposed ighted decoupling algorithm in the domain of system identification, and observe smaller model errors.
['Gabriel Hollander', 'Philippe Dreesen', 'Mariya Ishteva', 'Johan Schoukens']
I am writing a research paper on a idea Weighted tensor decomposition for approximate decoupling of multivariate polynomials
[{"from": "human", "value": "I am writing a research paper on a idea Weighted tensor decomposition for approximate decoupling of multivariate polynomials"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as ll as more intuitive understanding. For this, techniques based on tensor methods are known, but these have only been studied in the exact case. ,  generalize an existing method to the noisy case, by introducing a ight factor in the tensor decomposition. Finally,  apply the proposed ighted decoupling algorithm in the domain of system identification, and observe smaller model errors."}]