58 citations · 102 across the 11 of their papers we have counts for
6 papers · 1 filter
Decoupling multivariate functions using second-order information and tensors
Philippe Dreesen, Jeroen De Geeter, Mariya Ishteva
The power of multivariate functions is their ability to model a wide variety of phenomena, but have the disadvantages that they lack an intuitive or interpretable representation, a…
Decoupling multivariate polynomials: interconnections between tensorizations
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
Decoupling multivariate polynomials is useful for obtaining an insight into the workings of a nonlinear mapping, performing parameter reduction, or approximating nonlinear function…
Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels
Philippe Dreesen, David Westwick, Johan Schoukens +1
Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures…
Decoupling Multivariate Polynomials Using First-Order Information
Philippe Dreesen, Mariya Ishteva, Johan Schoukens
We present a method to decompose a set of multivariate real polynomials into linear combinations of univariate polynomials in linear forms of the input variables. The method procee…
Factorization approach to structured low-rank approximation with applications
Mariya Ishteva, Konstantin Usevich, Ivan Markovsky
We consider the problem of approximating an affinely structured matrix, for example a Hankel matrix, by a low-rank matrix with the same structure. This problem occurs in system ide…
A geometric Newton method for Oja's vector field
P. -A. Absil, M. Ishteva, L. De Lathauwer +1
Newton's method for solving the matrix equation runs up against the fact that its zeros are not isolated. This is due to a symmetry of by the action of…