Adaptive total variation and second-order total variation-based model for low-rank tensor completion
Author:
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics
Link
https://link.springer.com/content/pdf/10.1007/s11075-020-00876-y.pdf
Reference45 articles.
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3. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
4. Wen, Z.: Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm. Math. Program. Comput. 4(4), 333–361 (2012)
5. Candes, E.J., Plan, Y.: Matrix completion with noise. Proc. IEEE 98(6), 925–936 (2010)
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