Name
Abstract | ||
|---|---|---|
We develop Bayesian learning methods for low-rank matrix reconstruction and completion from linear measurements. For under-determined systems, the developed methods reconstruct low-rank matrices when neither the rank nor the noise power is known a priori. We derive relations between the proposed Bayesian models and low-rank promoting penalty functions. The relations justify the use of Kronecker st... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSP.2016.2597121 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Bayes methods,Estimation,Sparse matrices,Convex functions,Signal processing algorithms,Covariance matrices,Standards | Bayesian inference,Matrix (mathematics),Artificial intelligence,Sparse matrix,Covariance,Mathematical optimization,Estimation of covariance matrices,Pattern recognition,Expectation–maximization algorithm,Support vector machine,Algorithm,Low-rank approximation,Mathematics | Journal |
Volume | Issue | ISSN |
64 | 20 | 1053-587X |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Sundin | 1 | 39 | 6.18 |
| Cristian R. Rojas | 2 | 252 | 43.97 |
| Magnus Jansson | 3 | 452 | 56.14 |
| Saikat Chatterjee | 4 | 320 | 40.34 |