Structured Low-Rank Tensor Learning

Jayadev Naram, Tanmay Kumar Sinha, Pawan Kumar
5/13/2023
cs.LGmath.NA

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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.

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Cite this paper

@article{naram2023structured,
  title  = {Structured Low-Rank Tensor Learning},
  author = {Jayadev Naram and Tanmay Kumar Sinha and Pawan Kumar},
  year   = {2023},
  eprint = {2305.07967},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.07967v1}
}

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