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|a 10.1109/TPAMI.2022.3157083
|2 doi
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|a eng
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| 100 |
1 |
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|a Li, Xuelong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 06.04.2023
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|a Date Revised 06.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten- p norm, etc. They usually need many iterations to converge. Moreover, only the low-rank property of matrices is utilized in most existing models and several methods that incorporate other knowledge are quite time-consuming in practice. To address these issues, we propose a novel non-convex surrogate that can be optimized by closed-form solutions, such that it empirically converges within dozens of iterations. Besides, the optimization is parameter-free and the convergence is proved. Compared with the relaxation of rank, the surrogate is motivated by optimizing an upper-bound of rank. We theoretically validate that it is equivalent to the existing matrix completion models. Besides the low-rank assumption, we intend to exploit the column-wise correlation for matrix completion, and thus an adaptive correlation learning, which is scaling-invariant, is developed. More importantly, after incorporating the correlation learning, the model can be still solved by closed-form solutions such that it still converges fast. Experiments show the effectiveness of the non-convex surrogate and adaptive correlation learning
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Hongyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Rui
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 2 vom: 07. Feb., Seite 1981-1991
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:45
|g year:2023
|g number:2
|g day:07
|g month:02
|g pages:1981-1991
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|u http://dx.doi.org/10.1109/TPAMI.2022.3157083
|3 Volltext
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