Name
Title | ||
|---|---|---|
Iteratively reweighted least squares for reconstruction of low-rank matrices with linear structure |
Abstract | ||
|---|---|---|
This paper considers the problem of reconstructing low-rank matrices from undersampled measurements, when the matrix has a known linear structure. Based on the iterative reweighted least-squares approach, we develop an algorithm that exploits the linear structure in an efficient way that allows for reconstruction in highly undersampled scenarios. The method also enables inferring an appropriate regularization parameter value from the observations. The performance of the method is tested in a missing data recovery problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638909 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
least squares approximations,matrix algebra,signal reconstruction,signal sampling,iterative reweighted least-squares approach,linear structure,low-rank matrices reconstruction,missing data recovery problem,regularization parameter value,undersampled measurements,Cramér-Rao bound,low-rank matrix reconstruction,missing data recovery | Signal processing,Mathematical optimization,Pattern recognition,Matrix (mathematics),Computer science,Iteratively reweighted least squares,Linear complex structure,Regularization (mathematics),Artificial intelligence,Missing data,Linear least squares,Signal reconstruction | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.38 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dave Zachariah | 1 | 159 | 24.28 |
| Saikat Chatterjee | 2 | 24 | 5.32 |
| Magnus Jansson | 3 | 8 | 2.57 |