Structured Low-Rank Tensor Learning
Machine Learning
2023-05-16 v1 Numerical Analysis
Numerical Analysis
Abstract
We consider the problem of learning low-rank tensors from partial observations with structural constraints, and propose a novel factorization of such tensors, which leads to a simpler optimization problem. The resulting problem is an optimization problem on manifolds. We develop first-order and second-order Riemannian optimization algorithms to solve it. The duality gap for the resulting problem is derived, and we experimentally verify the correctness of the proposed algorithm. We demonstrate the algorithm on nonnegative constraints and Hankel constraints.
Cite
@article{arxiv.2305.07967,
title = {Structured Low-Rank Tensor Learning},
author = {Jayadev Naram and Tanmay Kumar Sinha and Pawan Kumar},
journal= {arXiv preprint arXiv:2305.07967},
year = {2023}
}
Comments
Accepted in OPT21, NeurIPS, 13 pages