TensorCompletion
A collection of tensor completion algorithms
Install / Use
/learn @shangqigao/TensorCompletionREADME
A collection of tensor completion algorithms
This is an official implementation of S-LRTC, BS-TMac, SpBCD, EPT-BCD, MCP-BCD, and SCAD-BCD via Matlab R2016. The implementation is based on FaLRTC, TMac , and geomCG, we thanks the authors for sharing their code. One can quickly test these algorithms as follows.
Quickly test "S-LRTC"
S-LRTC is an implementation of "A Mixture of Nuclear Norm and Matrix Factorization for Tensor Completion", one can quickly test the algorithm by running
test_S_LRTC.m
Quickly test "BS-TMac"
BS-TMac is an implementation of "Robust balancing scheme-based approach for tensor completion", one can quickly test the algorithm by running ```
test_BS_TMac.m
Quickly test "SpBCD"
SpBCD is an implementation of "Robust Schatten-p Norm Based Approach for Tensor Completion", one can quickly test the algorithm by running
test_SpBCD.m
Quickly test "EPT-BCD, MCP-BCD, and SCAD-BCD"
EPT-BCD, MCP-BCD, and SCAD-BCD is the implementation of "Robust approximations of low-rank minimization for tensor completion", one can quickly test the algorithms by running
test_EPT_MCP_SCAD.m
Related works
[1] Liu, J., Musialski, P., Wonka, P., Ye, J.P.: Tensor completion for estimating missing values in visual data. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 1126–1153 (2013) [link]
[2] Kressner, D., Steinlechner, M., Vandereycken, B.: Low-rank tensor completion by Riemannian optimization. BIT Numer. Math. 54(2), 447–468 (2014) [link]
[3] Xu, Y., Hao, R., Yin, W., Su, Z.: Parallel matrix factorization for low-rank tensor completion. Inverse Probl. Imaging 9(2), 601–624 (2015) [link]
Citation
If our works are useful in your research or publication, please cite the works:
[1] S. Gao and Q. Fan. A Mixture of Nuclear Norm and Matrix Factorization for Tensor Completion. J Sci Comput, 75:43–64, 2018. [link]
[2] S. Gao and Q. Fan. Robust balancing scheme-based approach for tensor completion. Neurocomputing, 330:328–336, 2019. [link]
[3] S. Gao and Q. Fan. Robust Schatten-p Norm Based Approach for Tensor Completion. J Sci Comput, 82:11, 2020. [link]
[4] S. Gao and X. Zhuang. Robust approximations of low-rank minimization for tensor completion. Neurocomputing, 379:319–333, 2020. [link]
Don't hesitate to contact us via shqgao@163.com, if you have any questions.
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