A singular value p-shrinkage thresholding algorithm for low rank matrix recovery
In this paper, we propose an iterative singular value p-shrinkage thresholding algorithm for
solving low rank matrix recovery problem, and also give its two accelerated versions using
randomized singular value decomposition. The convergence result of the proposed singular
value p-shrinkage thresholding algorithm is proved. Numerical results based on simulation
data and real data show the effectiveness of all the three proposed algorithms compared to
the existing state-of-the-art algorithms.
solving low rank matrix recovery problem, and also give its two accelerated versions using
randomized singular value decomposition. The convergence result of the proposed singular
value p-shrinkage thresholding algorithm is proved. Numerical results based on simulation
data and real data show the effectiveness of all the three proposed algorithms compared to
the existing state-of-the-art algorithms.
Abstract
In this paper, we propose an iterative singular value p-shrinkage thresholding algorithm for solving low rank matrix recovery problem, and also give its two accelerated versions using randomized singular value decomposition. The convergence result of the proposed singular value p-shrinkage thresholding algorithm is proved. Numerical results based on simulation data and real data show the effectiveness of all the three proposed algorithms compared to the existing state-of-the-art algorithms.
Springer
以上显示的是最相近的搜索结果。 查看全部搜索结果