File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Robust Low-Rank Tensor Recovery with Rectification and Alignment

TitleRobust Low-Rank Tensor Recovery with Rectification and Alignment
Authors
KeywordsADMM
alignment
Low-rank tensor recovery
proximal gradient
rectification
Issue Date2021
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, v. 43, n. 1, p. 238-255 How to Cite?
AbstractLow-rank tensor recovery in the presence of sparse but arbitrary errors is an important problem with many practical applications. In this work, we propose a general framework that recovers low-rank tensors, in which the data can be deformed by some unknown transformations and corrupted by arbitrary sparse errors. We give a unified presentation of the surrogate-based formulations that incorporate the features of rectification and alignment simultaneously, and establish worst-case error bounds of the recovered tensor. In this context, the state-of-the-art methods 'RASL' and 'TILT' can be viewed as two special cases of our work, and yet each only performs part of the function of our method. Subsequently, we study the optimization aspects of the problem in detail by deriving two algorithms, one based on the alternating direction method of multipliers (ADMM) and the other based on proximal gradient. We provide convergence guarantees for the latter algorithm, and demonstrate the performance of the former through in-depth simulations. Finally, we present extensive experimental results on public datasets to demonstrate the effectiveness and efficiency of the proposed framework and algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/327765
ISSN
2023 Impact Factor: 20.8
2023 SCImago Journal Rankings: 6.158
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Xiaoqin-
dc.contributor.authorWang, DI-
dc.contributor.authorZhou, Zhengyuan-
dc.contributor.authorMa, Yi-
dc.date.accessioned2023-05-08T02:26:39Z-
dc.date.available2023-05-08T02:26:39Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, v. 43, n. 1, p. 238-255-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10722/327765-
dc.description.abstractLow-rank tensor recovery in the presence of sparse but arbitrary errors is an important problem with many practical applications. In this work, we propose a general framework that recovers low-rank tensors, in which the data can be deformed by some unknown transformations and corrupted by arbitrary sparse errors. We give a unified presentation of the surrogate-based formulations that incorporate the features of rectification and alignment simultaneously, and establish worst-case error bounds of the recovered tensor. In this context, the state-of-the-art methods 'RASL' and 'TILT' can be viewed as two special cases of our work, and yet each only performs part of the function of our method. Subsequently, we study the optimization aspects of the problem in detail by deriving two algorithms, one based on the alternating direction method of multipliers (ADMM) and the other based on proximal gradient. We provide convergence guarantees for the latter algorithm, and demonstrate the performance of the former through in-depth simulations. Finally, we present extensive experimental results on public datasets to demonstrate the effectiveness and efficiency of the proposed framework and algorithms.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.subjectADMM-
dc.subjectalignment-
dc.subjectLow-rank tensor recovery-
dc.subjectproximal gradient-
dc.subjectrectification-
dc.titleRobust Low-Rank Tensor Recovery with Rectification and Alignment-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPAMI.2019.2929043-
dc.identifier.pmid31329109-
dc.identifier.scopuseid_2-s2.0-85097571113-
dc.identifier.volume43-
dc.identifier.issue1-
dc.identifier.spage238-
dc.identifier.epage255-
dc.identifier.eissn1939-3539-
dc.identifier.isiWOS:000597206900016-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats