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Proximal iteratively reweighted algorithm for low-rank matrix recovery
This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of nonconvex surrogate functions. Besides, this study...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758698/ https://www.ncbi.nlm.nih.gov/pubmed/29367824 http://dx.doi.org/10.1186/s13660-017-1602-x |