Autor:	Naram, Jayadev, Sinha, Tanmay Kumar, Kumar, Pawan
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Mathematics - Numerical Analysis
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in OPT21, NeurIPS, 13 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.07967 Zobrazit plný text záznamu View this record from Arxiv