7 papers
Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decomposi…
Tensor-based framework for training flexible neural networks
Yassine Zniyed, Konstantin Usevich, Sebastian Miron +1
Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN. In this work, we are…
Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich +3
Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with st…
On the convergence of Jacobi-type algorithms for Independent Component Analysis
Jianze Li, Konstantin Usevich, Pierre Comon
Jacobi-type algorithms for simultaneous approximate diagonalization of real (or complex) symmetric tensors have been widely used in independent component analysis (ICA) because of…
Jacobi-type algorithm for low rank orthogonal approximation of symmetric tensors and its convergence analysis
Jianze Li, Konstantin Usevich, Pierre Comon
In this paper, we propose a Jacobi-type algorithm to solve the low rank orthogonal approximation problem of symmetric tensors. This algorithm includes as a special case the well-kn…
Approximate matrix and tensor diagonalization by unitary transformations: convergence of Jacobi-type algorithms
Konstantin Usevich, Jianze Li, Pierre Comon
We propose a gradient-based Jacobi algorithm for a class of maximization problems on the unitary group, with a focus on approximate diagonalization of complex matrices and tensors…