activity
20162024
most citedTensor GMRES and Golub-Kahan Bidiagonalization methods via the Einstein product with applications to image and video processing

11 citations · 34 across the 29 of their papers we have counts for

collaborators

31 papers

math.NA2024

A rational Krylov methods for large scale linear multidimensional dynamical systems

Houda Barkouki, Khalide Jbilou

In this paper, we investigate the use of multilinear algebra for reducing the order of multidimensional linear time-invariant (MLTI) systems. Our main tools are tensor rational Kry…

math.NA2024★ 1 cited

Quaternion tensor low rank Quaternion tensor low-rank approximation using a family of non-convex norms

Alaeddine Zahir, Ahmed Ratnani, Khalide Jbilou

In this paper, we propose a new approaches for low rank approximation of quaternion tensors \cite{chen2019low,zhang1997quaternions,hamilton1866elements}. The first method uses quas…

math.NA2024

Non-negative Einstein tensor factorization for unmixing hyperspectral images

Anas El Hachimi, Khalide Jbilou, Ahmed Ratnani

In this manuscript, we introduce a tensor-based approach to Non-Negative Tensor Factorization (NTF). The method entails tensor dimension reduction through the utilization of the Ei…

math.NA2024★ 1 cited

Higher order multi-dimension reduction methods via Einstein product

Alaeddine Zahir, Khalide Jbilou, Ahmed Ratnani

This paper explores the extension of dimension reduction (DR) techniques to the multi-dimension case by using the Einstein product. Our focus lies on graph-based methods, encompass…

math.NA2024

Trace Ratio Based Manifold Learning with Tensor Data

Mohammed Bouallala, Franck Dufrenois, khalide jbilou +1

In this paper, we propose an extension of trace ratio based Manifold learning methods to deal with multidimensional data sets. Based on recent progress on the tensor-tensor product…

math.NA2024

Multidimensional extrapolated global proximal gradient and applications for image processing

Abdeslem Hafid Bentbib, Khalide Jbilou, Ridwane Tahiri

The proximal gradient method is a generic technique introduced to tackle the non-smoothness in optimization problems, wherein the objective function is expressed as the sum of a di…