Sparse Sampling for Inverse Problems With Tensors

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

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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
Volume67
Issue number12
DOIs
StatePublished - Jun 15 2019
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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