Sparse Sampling for Inverse Problems With Tensors

Guillermo Ortiz-Jimenez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


We 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.
Original languageEnglish (US)
Pages (from-to)3272-3286
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number12
StatePublished - Jun 15 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-03-12
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This 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].
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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