Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space
Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Alt...
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| Autor*in: |
Yu, Jinshi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 35(2022), 9 vom: 04. Dez., Seite 7003-7016 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2022 ; number:9 ; day:04 ; month:12 ; pages:7003-7016 |
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| DOI / URN: |
10.1007/s00521-022-08023-5 |
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| 520 | |a Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. | ||
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10.1007/s00521-022-08023-5 doi (DE-627)OLC2134212578 (DE-He213)s00521-022-08023-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yu, Jinshi verfasserin aut Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. Tensor completion Tensor ring decomposition Tensor ring rank Image/video completion. Zou, Tao (orcid)0000-0001-7328-5703 aut Zhou, Guoxu aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 9 vom: 04. Dez., Seite 7003-7016 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:9 day:04 month:12 pages:7003-7016 https://doi.org/10.1007/s00521-022-08023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 9 04 12 7003-7016 |
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10.1007/s00521-022-08023-5 doi (DE-627)OLC2134212578 (DE-He213)s00521-022-08023-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yu, Jinshi verfasserin aut Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. Tensor completion Tensor ring decomposition Tensor ring rank Image/video completion. Zou, Tao (orcid)0000-0001-7328-5703 aut Zhou, Guoxu aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 9 vom: 04. Dez., Seite 7003-7016 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:9 day:04 month:12 pages:7003-7016 https://doi.org/10.1007/s00521-022-08023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 9 04 12 7003-7016 |
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10.1007/s00521-022-08023-5 doi (DE-627)OLC2134212578 (DE-He213)s00521-022-08023-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yu, Jinshi verfasserin aut Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. Tensor completion Tensor ring decomposition Tensor ring rank Image/video completion. Zou, Tao (orcid)0000-0001-7328-5703 aut Zhou, Guoxu aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 9 vom: 04. Dez., Seite 7003-7016 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:9 day:04 month:12 pages:7003-7016 https://doi.org/10.1007/s00521-022-08023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 9 04 12 7003-7016 |
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10.1007/s00521-022-08023-5 doi (DE-627)OLC2134212578 (DE-He213)s00521-022-08023-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yu, Jinshi verfasserin aut Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. Tensor completion Tensor ring decomposition Tensor ring rank Image/video completion. Zou, Tao (orcid)0000-0001-7328-5703 aut Zhou, Guoxu aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 9 vom: 04. Dez., Seite 7003-7016 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:9 day:04 month:12 pages:7003-7016 https://doi.org/10.1007/s00521-022-08023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 9 04 12 7003-7016 |
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10.1007/s00521-022-08023-5 doi (DE-627)OLC2134212578 (DE-He213)s00521-022-08023-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yu, Jinshi verfasserin aut Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. Tensor completion Tensor ring decomposition Tensor ring rank Image/video completion. Zou, Tao (orcid)0000-0001-7328-5703 aut Zhou, Guoxu aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 9 vom: 04. Dez., Seite 7003-7016 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:9 day:04 month:12 pages:7003-7016 https://doi.org/10.1007/s00521-022-08023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 9 04 12 7003-7016 |
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Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space |
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Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In recent years, tensor ring (TR) decomposition has drawn a lot of attention and was successfully applied to tensor completion problem, due to its more compact representation ability. As well known, both global and local structural information is important for tensor completion problem. Although the existing TR-based completion algorithms obtain the impressive performance in visual-data inpainting by using low-rank global structure information, most of them didn’t take into account local smooth property which is often exhibited in visual data. To further improve visual-data inpainting performance, both low-rank and piecewise smooth structures are incorporated in our model. Instead of directly applying local smooth constraint on the data surface, we impose the smoothness on its latent TR-space, which greatly reduces computational cost especially for large-scale data. Extensive experiments on real-world visual data show that our model not only obtains the state-of-the-art performance, but also is rather stable to the TR-ranks owing to the local smooth constraint. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Low tensor-ring rank completion: parallel matrix factorization with smoothness on latent space |
| url |
https://doi.org/10.1007/s00521-022-08023-5 |
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| author2 |
Zou, Tao Zhou, Guoxu |
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| doi_str |
10.1007/s00521-022-08023-5 |
| up_date |
2024-07-04T00:01:37.044Z |
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