Minimum \( n\)-rank approximation via iterative hard thresholding

DOI10.1016/j.amc.2015.01.099zbMath1338.15053arXiv1311.4291OpenAlexW2044074821MaRDI QIDQ299732

Lei Yang, Min Zhang, Zheng-Hai Huang

Publication date: 22 June 2016

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1311.4291

zbMATH Keywords

compressed sensingiterative hard thresholdinglow-\( n\)-rank tensor recoverytensor completion

Mathematics Subject Classification ID

Multilinear algebra, tensor calculus (15A69)

Related Items (4)

Continuity, differentiability and semismoothness of generalized tensor functions ⋮ $N$-Dimensional Tensor Completion for Nuclear Magnetic Resonance Relaxometry ⋮ Iterative \(p\)-shrinkage thresholding algorithm for low Tucker rank tensor recovery ⋮ Low-Rank Tensor Recovery using Sequentially Optimal Modal Projections in Iterative Hard Thresholding (SeMPIHT)

Uses Software

• PROPACK
• TensorToolbox
• N-way Toolbox
• ADMiRA

Cites Work

• Unnamed Item
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