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Relevance Singular Vector Machine for low rank matrix sensing
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: 0000-0003-2638-6047
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), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
2014 (English)In: Signal Processing and Communications (SPCOM), 2014 International Conference on, IEEE conference proceedings, 2014, 1-5 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we develop a new Bayesian inference method for lowrank matrix reconstruction. We call the new method the RelevanceSingular Vector Machine (RSVM) where appropriate priors are definedon the singular vectors of the underlying matrix to promotelow rank. To accelerate computations, a numerically efficient approximationis developed. The proposed algorithms are applied tomatrix completion and matrix reconstruction problems and their performanceis studied numerically.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 1-5 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-147601DOI: 10.1109/SPCOM.2014.6983925ISI: 000364936400015Scopus ID: 2-s2.0-84920742632ISBN: 978-1-4799-4666-2 (print)OAI: oai:DiVA.org:kth-147601DiVA: diva2:730904
Conference
International Conference on Signal Processing and Communications (SPCOM), 22-25 July 2014,Bangalore, India
Note

QC 20150413

Available from: 2014-06-30 Created: 2014-06-30 Last updated: 2015-12-18Bibliographically approved

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fulltext(972 kB)118 downloads
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Chatterjee, SaikatJansson, MagnusRojas, Cristian

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