Alternating strategies with internal ADMM for low-rank matrix reconstruction
2016 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 121, 153-159 p.Article in journal (Refereed) PublishedText
This paper focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover low-rank matrices with linear parameterized structures, such as Hankel matrices. The use of ADMM helps to improve the estimate in each iteration due to its capability of incorporating information about the direction of estimates achieved in previous iterations. We show that merging these two alternating strategies leads to a better performance and less consumed time than the existing alternating least squares (ALS) strategy. The improved performance is verified via numerical simulations with varying sampling rates and real applications.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 121, 153-159 p.
ADMM, Alternating strategies, Least squares, Low-rank matrix reconstruction
Control Engineering Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-180899DOI: 10.1016/j.sigpro.2015.11.002ISI: 000369193600013ScopusID: 2-s2.0-84949761064OAI: oai:DiVA.org:kth-180899DiVA: diva2:899484
FunderSwedish Research Council, 621-2011-5847
QC 20160202. QC 201603042016-02-022016-01-252016-03-04Bibliographically approved