5 accepted papers
Low-rank matrix completion (LRMC) is a classical model in both computer vision (CV) and machine learning, and has been successfully applied to various real applications. In the recent CV tasks, the completion is usually employed on the variants of data, such as "non-local" or filtered, rather than t…
Tensor train decomposition is a powerful representation for high-order tensors, which has been successfully applied to various machine learning tasks in recent years. In this paper, we study a more generalized tensor decomposition with a ring-structured network by employing circular multilinear prod…
Dimensionality reduction is an essential technique for multiway large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when faci…
In recent studies, tensor ring (TR) decomposition has shown to be effective in data compression and representation. However, the existing TR-based completion methods only exploit the global low-rank property of the visual data. When applying them to remote sensing (RS) image processing, the spatial…
In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tensor order. We propose an algorithm named Sparse Tensor-train Optimization (STTO) which considers incomplete data as sparse…