Optimization on the Hierarchical Tucker manifold - applications to tensor completion

TitleOptimization on the {Hierarchical} {Tucker} manifold - applications to tensor completion
Publication TypeJournal Article
Year of Publication2015
AuthorsCurt Da Silva, Felix J. Herrmann
JournalLinear Algebra and its Applications
Volume481
Page131-173
Month09
PublisherUBC
Keywordsdifferential geometry, Gauss–Newton, hierarchical tucker tensors, low-rank tensor, Riemannian manifold optimization, tensor completion
Abstract

In this work, we develop an optimization framework for problems whose solutions are well-approximated by Hierarchical Tucker (HT) tensors, an efficient structured tensor format based on recursive subspace factorizations. By exploiting the smooth manifold structure of these tensors, we construct standard optimization algorithms such as Steepest Descent and Conjugate Gradient for completing tensors from missing entries. Our algorithmic framework is fast and scalable to large problem sizes as we do not require SVDs on the ambient tensor space, as required by other methods. Moreover, we exploit the structure of the Gramian matrices associated with the HT format to regularize our problem, reducing overfitting for high subsampling ratios. We also find that the organization of the tensor can have a major impact on completion from realistic seismic acquisition geometries. These samplings are far from idealized randomized samplings that are usually considered in the literature but are realizable in practical scenarios. Using these algorithms, we successfully interpolate large-scale seismic data sets and demonstrate the competitive computational scaling of our algorithms as the problem sizes grow.

Notes

(Linear Algebra and its Applications)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Journals/LinearAlgebraAndItsApplications/2015/dasilva2014htuck/dasilva2014htuck.pdf
DOI10.1016/j.laa.2015.04.015
URL1

http://www.sciencedirect.com/science/article/pii/S0024379515002530

Citation Keydasilva2014htuck