Alternating Least-Squares for Low-Rank Matrix Reconstruction
2012 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 19, no 4, 231-234 p.Article in journal (Refereed) Published
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any low-rank matrix, it is also capable of exploiting a-priori knowledge of matrix structure. In particular, we consider linearly structured matrices, such as Hankel and Toeplitz, as well as positive semidefinite matrices. The performance of the algorithm, referred to as alternating least-squares (ALS), is evaluated by simulations and compared to the Cramer-Rao bounds.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2012. Vol. 19, no 4, 231-234 p.
Cramer-Rao bound, least squares, low-rank matrix reconstruction, structured matrices
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-92992DOI: 10.1109/LSP.2012.2188026ISI: 000301201600003ScopusID: 2-s2.0-84858034120OAI: oai:DiVA.org:kth-92992DiVA: diva2:514938
FunderICT - The Next Generation
QC 201204112012-10-052012-04-102013-04-11Bibliographically approved