Sparse Sampling for Inverse Problems with Tensors

Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus
6/28/2018
cs.ITeess.SP

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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 multi-antenna communications to graph signal processing, to validate the developed theory.

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Cite this paper

@article{ortizjimnez2018sparse,
  title  = {Sparse Sampling for Inverse Problems with Tensors},
  author = {Guillermo Ortiz-Jiménez and Mario Coutino and Sundeep Prabhakar Chepuri and Geert Leus},
  year   = {2018},
  eprint = {1806.10976},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1806.10976}
}

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