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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2015.2503238
|2 doi
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|a pubmed24n0850.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Qing Liu
|e verfasserin
|4 aut
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| 245 |
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|a A Truncated Nuclear Norm Regularization Method Based on Weighted Residual Error for Matrix Completion
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|c 2016
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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 18.03.2016
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|a Date Revised 11.03.2016
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Low-rank matrix completion aims to recover a matrix from a small subset of its entries and has received much attention in the field of computer vision. Most existing methods formulate the task as a low-rank matrix approximation problem. A truncated nuclear norm has recently been proposed as a better approximation to the rank of matrix than a nuclear norm. The corresponding optimization method, truncated nuclear norm regularization (TNNR), converges better than the nuclear norm minimization-based methods. However, it is not robust to the number of subtracted singular values and requires a large number of iterations to converge. In this paper, a TNNR method based on weighted residual error (TNNR-WRE) for matrix completion and its extension model (ETNNR-WRE) are proposed. TNNR-WRE assigns different weights to the rows of the residual error matrix in an augmented Lagrange function to accelerate the convergence of the TNNR method. The ETNNR-WRE is much more robust to the number of subtracted singular values than the TNNR-WRE, TNNR alternating direction method of multipliers, and TNNR accelerated proximal gradient with Line search methods. Experimental results using both synthetic and real visual data sets show that the proposed TNNR-WRE and ETNNR-WRE methods perform better than TNNR and Iteratively Reweighted Nuclear Norm (IRNN) methods
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhihui Lai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zongwei Zhou
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fangjun Kuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhong Jin
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 1 vom: 01. Jan., Seite 316-30
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:25
|g year:2016
|g number:1
|g day:01
|g month:01
|g pages:316-30
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2015.2503238
|3 Volltext
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