Enhanced Sparsity Prior Model for Low-Rank Tensor Completion

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 11; pp. 4567 - 4581
• Main Authors: Xue, Jize, Zhao, Yongqiang, Liao, Wenzhi, Chan, Jonathan Cheung-Wai, Kong, Seong G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Enhanced sparsity prior; factor gradient sparsity; global and local sparsities; Hyperspectral imaging; low-rank (LR) tensor completion (TC); Mathematical analysis; Matrix decomposition; Minimization; Nuclear measurements; Smoothness; Sparsity; Task analysis; Tensors; Tucker decomposition

Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data lies in the global subspace. The so-called global sparsity prior is measured by the tensor nuclear norm. Such assumption is not reliable in recovering low-rank (LR) tensor data, especially when consi...