Bart Vandereycken

Low-Rank tensor completion by Riemannian optimization

by , ,

Abstract:

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. More specifically, a variant of the nonlinear conjugate gradient method is developed. Paying particular attention to efficient implementation, our algorithm scales linearly in the size of the tensor. Examples with synthetic data demonstrate good recovery even if the vast majority of the entries are unknown. We illustrate the use of the developed algorithm for the recovery of multidimensional images and for the approximation of multivariate functions.

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Reference:

D. Kressner, M. Steinlechner, B. Vandereycken, "Low-Rank tensor completion by Riemannian optimization", In BIT Numer. Math., vol. 54, no. 2, pp. 447-468, 2014.

Bibtex Entry:

@article{Kressner_SV_2013,
    Abstract = {In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. More specifically, a variant of the nonlinear conjugate gradient method is developed. Paying particular attention to efficient implementation, our algorithm scales linearly in the size of the tensor. Examples with synthetic data demonstrate good recovery even if the vast majority of the entries are unknown. We illustrate the use of the developed algorithm for the recovery of multidimensional images and for the approximation of multivariate functions.},
    Author = {Kressner, D. and Steinlechner, M. and Vandereycken, B.},
    Doi = {10.1007/s10543-013-0455-z},
    Journal = {BIT Numer. Math.},
    Number = {2},
    Pages = {447--468},
    Pdf = {http://www.unige.ch/math/vandereycken/papers/preprint_Kressner_SV_2013.pdf},
    Title = {Low-Rank tensor completion by {R}iemannian optimization},
    Volume = {54},
    Year = {2014}}