Sparse Sampling for Inverse Problems With Tensors

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.


Published in:
Ieee Transactions On Signal Processing, 67, 12, 3272-3286
Year:
Jun 15 2019
Publisher:
Piscataway, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN:
1053-587X
1941-0476
Keywords:
Laboratories:




 Record created 2019-06-18, last modified 2019-07-10


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