Linear total variation approximate regularized nuclear norm optimization for matrix completion

DOI10.1155/2014/765782zbMath1474.94042OpenAlexW2089138312WikidataQ59041343 ScholiaQ59041343MaRDI QIDQ1724931

Lotfi Senhadji, Yang Chen, Lu Wang, Huazhong Shu, Jiasong Wu, X. Han

Publication date: 14 February 2019

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2014/765782

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Matrix completion problems (15A83)

Related Items (1)

Enhancing matrix completion using a modified second-order total variation

Cites Work

• Nonlinear total variation based noise removal algorithms
• Exact matrix completion via convex optimization
• A Singular Value Thresholding Algorithm for Matrix Completion
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
• Matrix Completion From a Few Entries
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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