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Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction
KTH, School of Electrical Engineering (EES), Signal Processing.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0003-2638-6047
2016 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 20, 5327-5339 p.Article in journal (Refereed) Published
Abstract [en]

We develop Bayesian learning methods for low-rank matrix reconstruction and completion from linear measurements. For under-determined systems, the developed methods reconstruct low-rank matrices when neither the rank nor the noise power is known a priori. We derive relations between the proposed Bayesian models and low-rank promoting penalty functions. The relations justify the use of Kronecker structured covariance matrices in a Gaussian-based prior. In the methods, we use expectation maximization to learn the model parameters. The performance of the methods is evaluated through extensive numerical simulations on synthetic and real data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 64, no 20, 5327-5339 p.
Keyword [en]
Machine learning, Bayes methods, compressed sensing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-193155DOI: 10.1109/TSP.2016.2597121ISI: 000382677100011Scopus ID: 2-s2.0-84985021194OAI: oai:DiVA.org:kth-193155DiVA: diva2:1037535
Note

QC 20161017

Available from: 2016-10-17 Created: 2016-09-30 Last updated: 2017-11-29Bibliographically approved

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