arXiv:2609.24679v1 Announce Type: new
Abstract: We consider the recovery of low-multilinear-rank tensors from linear measurements and propose an adaptive block-weighted modewise Riemannian gradient d...
By Yushi Zhou, Feng Zhang
Tensor Completion using Subspace Information (TCSI) is an algorithm that leverages side information by estimating a subspace and reformulating tensor completion as a matrix regression problem. Theoretical analysis shows that accurate subspace information reduces sample complexity to nearly linear in the uncoupled ambient dimensions and relaxes signal-to-noise ratio requirements compared to existing guarantees. Numerical simulations and an application to reconstructing global Total Electron Content (TEC) maps demonstrate lower reconstruction errors than competing methods.
By Jingyang Li, Michael K. Ng
arXiv:2602. 05869v2 Announce Type: replace-cross Abstract: We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion.
By Hengrui Luo, Anna Ma, Ludovic Stephan, Yizhe Zhu
arXiv:2603.25911v2 Announce Type: replace-cross
Abstract: Tensor-on-tensor regression is an important tool for the analysis of tensor data, aiming to predict a set of response tensors from a correspo...
By Mehdi Hirari, Fabio Centofanti, Mia Hubert, Stefan Van Aelst
arXiv:2606. 31061v1 Announce Type: cross Abstract: Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data.
By Hiroki Takeda, Yuto Miyatake, Daisuke Furihata
arXiv:2606. 08565v1 Announce Type: cross Abstract: Tensor networks provide efficient representations for compressing large neural networks.
By Toshiaki Koike-Akino, Jing Liu, Ye Wang