Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161775
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dc.contributor.authorChai, Woon Hueien_US
dc.contributor.authorHo, Shen-Shyangen_US
dc.contributor.authorQuek, Hiok Chaien_US
dc.date.accessioned2022-09-20T00:41:13Z-
dc.date.available2022-09-20T00:41:13Z-
dc.date.issued2022-
dc.identifier.citationChai, W. H., Ho, S. & Quek, H. C. (2022). Representation recovery via L₁-norm minimization with corrupted data. Information Sciences, 595, 395-426. https://dx.doi.org/10.1016/j.ins.2021.11.074en_US
dc.identifier.issn0020-0255en_US
dc.identifier.urihttps://hdl.handle.net/10356/161775-
dc.description.abstractThis paper studies the recovery probability of a state-of-the-art sparse recovery method, the optimization problem of YALL1, which has been rigorously used in face recognition, dense error correction, anomaly detection, etc. This work generalizes a theoretical work which is based on a special case of the optimization problem of YALL1. Furthermore, the new results cover more practical cases which do not fulfill the bouquet model proposed in the early work. The results also show that not only the special case but also some other cases of the optimization problem of YALL1; which fulfill certain conditions; can also recover any sufficiently sparse coefficient vector x when the fraction of the support of the error e is bounded away from 1 and the support of x is a very small fraction of its dimension m as m becomes large. The trade-off parameter λ in the optimization problem of YALL1 allows the recovery probability to be optimally tuned than the special case. Experimental results also show that the optimization problem of YALL1 (the Eq. (7)) with primal augmented Lagrangian optimization technique outperforms the state-of-the-art sparse recovery methods using their corresponding optimization techniques in term of the speed.en_US
dc.language.isoenen_US
dc.relation.ispartofInformation Sciencesen_US
dc.rights© 2021 Elsevier Inc. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleRepresentation recovery via L₁-norm minimization with corrupted dataen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.researchRolls-Royce@NTU Corporate Laben_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1016/j.ins.2021.11.074-
dc.identifier.scopus2-s2.0-85126057253-
dc.identifier.volume595en_US
dc.identifier.spage395en_US
dc.identifier.epage426en_US
dc.subject.keywordsError Correctionen_US
dc.subject.keywordsSparse Representationen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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