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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Babacan, S.D. Luessi, M. Molina, R. Katsaggelos, A.K. |
| Copyright Year | 2011 |
| Description | Author affiliation: Beckman Institute, University of Illinois at Urbana-Champaign, USA (Babacan, S.D.) || Departamento de Ciencias, de la Computación e I.A., Universidad de Granada, Spain (Molina, R.) || Department of Electrical Engineering and Computer Science, Northwestern University, USA (Luessi, M.; Katsaggelos, A.K.) |
| Abstract | There has been a significant interest in the recovery of low-rank matrices from an incomplete of measurements, due to both theoretical and practical developments demonstrating the wide applicability of the problem. A number of methods have been developed for this recovery problem, however, a principled method for choosing the unknown target rank is generally missing. In this paper, we present a recovery algorithm based on sparse Bayesian learning (SBL) and automatic relevance determination principles. Starting from a matrix factorization formulation and enforcing the low-rank constraint in the estimates as a sparsity constraint, we develop an approach that is very effective in determining the correct rank while providing high recovery performance. We provide empirical results and comparisons with current state-of-the-art methods that illustrate the potential of this approach. |
| Starting Page | 2188 |
| Ending Page | 2191 |
| File Size | 126671 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457705380 |
| ISSN | 15206149 |
| e-ISBN | 9781457705397 |
| e-ISBN | 9781457705373 |
| DOI | 10.1109/ICASSP.2011.5946762 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-22 |
| Publisher Place | Czech Republic |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Approximation methods Estimation Sparse matrices Noise Machine learning Optimization automatic relevance determination Low-rank matrix completion |
| Content Type | Text |
| Resource Type | Article |
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