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eess.SP2021
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…
eess.SP2020
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…
eess.SP2018
Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms
Clémence Prévost, Konstantin Usevich, Pierre Comon +1
We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the c…