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… Expand

Low-rank tensor variants of short-recurrence Krylov subspace methods are developed that benefit from the fact that x(\alpha) can be well approximated by a tensor of low rank.Expand

In this paper, we propose to represent structured data as a sparse combination of localized functions that live on a graph that is a priori unknown.Expand

We propose novel, highly scalable algorithms based on a combination of the canonical polyadic (CP) tensor format with block coordinate descent methods.Expand

Signal-processing on graphs has developed into a very active field of research during the last decade. In particular, the number of applications using frames constructed from graphs, like wavelets on… Expand