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Alternating Least-Squares for Low-Rank Matrix Reconstruction
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
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
Abstract [en]

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.
Keyword [en]
Cramer-Rao bound, least squares, low-rank matrix reconstruction, structured matrices
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-92992DOI: 10.1109/LSP.2012.2188026ISI: 000301201600003Scopus ID: 2-s2.0-84858034120OAI: oai:DiVA.org:kth-92992DiVA: diva2:514938
Funder
ICT - The Next Generation
Note

QC 20120411

Available from: 2012-10-05 Created: 2012-04-10 Last updated: 2017-12-07Bibliographically approved

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