Low-rank matrix completion by variational sparse Bayesian learning. Babacan, S. D., Luessi, M., Molina, R., & Katsaggelos, A. K. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2188–2191, may, 2011. IEEE, IEEE. ![Low-rank matrix completion by variational sparse Bayesian learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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. © 2011 IEEE.
@inproceedings{babacan2011low,
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. {\textcopyright} 2011 IEEE.},
author = {Babacan, S. Derin and Luessi, Martin and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2011.5946762},
isbn = {978-1-4577-0538-0},
issn = {15206149},
keywords = {Bayesian methods,Low-rank matrix completion,automatic relevance determination},
month = {may},
organization = {IEEE},
pages = {2188--2191},
publisher = {IEEE},
title = {{Low-rank matrix completion by variational sparse Bayesian learning}},
url = {http://ieeexplore.ieee.org/document/5946762/},
year = {2011}
}
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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. 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