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dc.contributor.authorOrtiz-Jimenez, Guillermo
dc.contributor.authorCoutino, Mario
dc.contributor.authorChepuri, Sundeep Prabhakar
dc.contributor.authorLeus, Geert
dc.date.accessioned2021-03-11T12:26:24Z
dc.date.available2021-03-11T12:26:24Z
dc.date.issued2019-06-15
dc.identifier.citationOrtiz-Jimenez, G., Coutino, M., Chepuri, S. P., & Leus, G. (2019). Sparse Sampling for Inverse Problems With Tensors. IEEE Transactions on Signal Processing, 67(12), 3272–3286. doi:10.1109/tsp.2019.2914879
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.doi10.1109/tsp.2019.2914879
dc.identifier.urihttp://hdl.handle.net/10754/668095
dc.description.abstractWe consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop low-complexity greedy algorithms based on submodular optimization methods to compute near-optimal sampling sets. We present several numerical examples, ranging from multiantenna communications to graph signal processing, to validate the developed theory.
dc.description.sponsorshipThis work was supported in part by the ASPIRE project (Project 14926 within the STW OTP programme), in part by the Netherlands Organization for Scientific Research, and in part by the KAUST-MIT-TUD consortium under Grant OSR-2015-Sensors-2700. The work of G. Ortiz-Jiménez was supported by a fellowship from Fundación Bancaria “la Caixa.” The work of M. Coutino was supported by CONACYT. This paper was presented in part at the Sixth IEEE Global Conference on Signal and Information Processing, Anaheim, CA, November 2018 [1].
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8705331/
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleSparse Sampling for Inverse Problems With Tensors
dc.typeArticle
dc.identifier.journalIEEE Transactions on Signal Processing
dc.eprint.versionPost-print
dc.contributor.institutionFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, CD, The Netherlands
dc.contributor.institutionInstitute of Electrical Engineering, Ecole Polytechnique Fdrale de Lausanne, Lausanne 1015, Switzerland
dc.contributor.institutionDepartment of Electrical Communications Engineering, Indian Institute of Science, Bangalore 560012, India
dc.identifier.volume67
dc.identifier.issue12
dc.identifier.pages3272-3286
dc.identifier.arxivid1806.10976
kaust.grant.numberOSR-2015-Sensors-2700
dc.identifier.eid2-s2.0-85066879521
kaust.acknowledged.supportUnitOSR-2015-Sensors-2700


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