Low-Rank Matrix Recovery Via Nonconvex Optimization Methods with Application to Errors-in-Variables Matrix Regression
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DOI: 10.1007/s10957-025-02660-1
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- Wu, Jie & Zheng, Zemin & Li, Yang & Zhang, Yi, 2020. "Scalable interpretable learning for multi-response error-in-variables regression," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
- Radu-Alexandru Dragomir & Alexandre d’Aspremont & Jérôme Bolte, 2021. "Quartic First-Order Methods for Low-Rank Minimization," Journal of Optimization Theory and Applications, Springer, vol. 189(2), pages 341-363, May.
- Xin Li & Dongya Wu, 2024. "Low-rank matrix estimation via nonconvex optimization methods in multi-response errors-in-variables regression," Journal of Global Optimization, Springer, vol. 88(1), pages 79-114, January.
- Shuvomoy Das Gupta & Bartolomeo Stellato & Bart P. G. Parys, 2024. "Exterior-Point Optimization for Sparse and Low-Rank Optimization," Journal of Optimization Theory and Applications, Springer, vol. 202(2), pages 795-833, August.
- Hua Zhou & Lexin Li & Hongtu Zhu, 2013. "Tensor Regression with Applications in Neuroimaging Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 540-552, June.
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Keywords
Low-rank matrix recovery; Nonconvex optimization; Nonconvex regularization; Recovery bounds; Errors-in-variables matrix regression;All these keywords.
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