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 also has shown that the proximal iteratively reweighted algorithm lessens the objective function value monotonically, and any limit point is a stationary point theoretically.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758698 | PMC |
| http://dx.doi.org/10.1186/s13660-017-1602-x | DOI Listing |
Publication Analysis
Top Keywords
proximal iteratively
12
iteratively reweighted
12
reweighted algorithm
12
low-rank matrix
8
algorithm low-rank
4
matrix recovery
4
recovery paper
4
paper proposes
4
proposes proximal
4
algorithm recover
4
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!