Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction
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
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.
Machine learning, Bayes methods, compressed sensing
IdentifiersURN: urn:nbn:se:kth:diva-193155DOI: 10.1109/TSP.2016.2597121ISI: 000382677100011ScopusID: 2-s2.0-84985021194OAI: oai:DiVA.org:kth-193155DiVA: diva2:1037535
QC 201610172016-10-172016-09-302016-10-17Bibliographically approved