Weighted hybrid truncated norm regularization method for low-rank matrix completion
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s11075-023-01513-0.pdf
Reference23 articles.
1. Ma, T.H., Lou, Y., Huang, T.Z.: Truncated l1 − 2 models for sparse recovery and rank minimization. SIAM J. Imaging Sci. 10 (3), 1346–1380 (2017)
2. Zhao, X.L., Wang, F., Huang, T.Z., et al.: Deblurring and sparse unmixing for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 51(7), 4045–4058 (2013)
3. Zhao, X.L., Xu, W.H., Jiang, T.X., et al.: Deep plug-and-play prior for low-rank tensor completion. Neurocomputing 400, 137–149 (2020)
4. Zhao, X.L., Zhang, H., Jiang, T.X., et al.: Fast algorithm with theoretical guarantees for constrained low-tubal-rank tensor recovery in hyperspectral images denoising. Neurocomputing 413, 397–409 (2020)
5. Jannach, D., Resnick, P., Tuzhilin, A., et al.: Recommender systems—beyond matrix completion. Commun. ACM 59(11), 94–102 (2016)
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