Robust PCA using nonconvex rank approximation and sparse regularizer

DOI10.1007/S00034-019-01310-YzbMath1452.94012OpenAlexW2990436289WikidataQ126747164 ScholiaQ126747164MaRDI QIDQ2193603

Jing Dong, Zhichao Xue, Wenwu Wang

Publication date: 20 August 2020

Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://surrey-researchmanagement.esploro.exlibrisgroup.com/view/delivery/44SUR_INST/12140079080002346/13140329830002346

zbMATH Keywords

robust principal component analysislow-ranksparse prior\( \ell_{\gamma}\)-norm

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Uses Software

• LMaFit
• RASL

Cites Work

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• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Image Super-Resolution Via Sparse Representation
• Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization

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