Add to ORKGAbstract. We present a tensor-based method to decompose a given set of multivariate functions into linear combinations of a set of multivari-ate functions of linear forms of the input variables. The method pro-ceeds by forming a three-way array (tensor) by stacking Jacobian matrix evaluations of the function behind each other. It is shown that a block-term decomposition of this tensor provides the necessary information to block-decouple the given function into a set of functions with small input-output dimensionality. The method is validated on a numerical example
The widespread use of multisensor technology and the emergence of big data sets have highlighted the...
The paper first shows that Kruskal tensors with matrix factors derived from orthogonal ternary vecto...
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges arou...
Abstract. We present a tensor-based method to decompose a given set of multivariate functions into l...
We review a method that decouples multivariate functions into linear combinations of a set of univar...
Abstract. We present a method to decompose a set of multivariate real polynomials into linear combin...
© 2019 Elsevier B.V. Decoupling multivariate polynomials is useful for obtaining an insight into the...
\u3cp\u3eMultivariate polynomials are often used to model nonlinear behavior, e.g., in parallel Wien...
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are...
special session "Tensor Computations in Linear and Multilinear Algebra"Tensor decompositions permit ...
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are...
In this paper, we propose a new method for multivariate function approximation that generalized the ...
International audienceIn this paper, we present an improved method for decomposing multivariate poly...
Decompositions of higher-order tensors are becoming more and more important in signal processing, da...
Low-rank tensors are an established framework for the parametrization of multivariate polynomials. W...
The widespread use of multisensor technology and the emergence of big data sets have highlighted the...
The paper first shows that Kruskal tensors with matrix factors derived from orthogonal ternary vecto...
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges arou...
Abstract. We present a tensor-based method to decompose a given set of multivariate functions into l...
We review a method that decouples multivariate functions into linear combinations of a set of univar...
Abstract. We present a method to decompose a set of multivariate real polynomials into linear combin...
© 2019 Elsevier B.V. Decoupling multivariate polynomials is useful for obtaining an insight into the...
\u3cp\u3eMultivariate polynomials are often used to model nonlinear behavior, e.g., in parallel Wien...
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are...
special session "Tensor Computations in Linear and Multilinear Algebra"Tensor decompositions permit ...
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are...
In this paper, we propose a new method for multivariate function approximation that generalized the ...
International audienceIn this paper, we present an improved method for decomposing multivariate poly...
Decompositions of higher-order tensors are becoming more and more important in signal processing, da...
Low-rank tensors are an established framework for the parametrization of multivariate polynomials. W...
The widespread use of multisensor technology and the emergence of big data sets have highlighted the...
The paper first shows that Kruskal tensors with matrix factors derived from orthogonal ternary vecto...
Tensor decomposition methods and multilinear algebra are powerful tools to cope with challenges arou...