Iteratively Reweighted Least Squares for Reconstruction of Low-Rank Matrices with Linear Structure
2013 (English)In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE conference proceedings, 2013, 6456-6460 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 6456-6460 p.
, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Cramér-Rao bound, low-rank matrix reconstruction, missing data recovery
IdentifiersURN: urn:nbn:se:kth:diva-123558DOI: 10.1109/ICASSP.2013.6638909ISI: 000329611506124ScopusID: 2-s2.0-84890543006ISBN: 978-147990356-6OAI: oai:DiVA.org:kth-123558DiVA: diva2:627611
The 38th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 26 - 31, 2013
QC 201307232013-06-122013-06-122014-02-21Bibliographically approved