Sparse Sampling for Inverse Problems with Tensors
Information Theory
2019-06-26 v1 Signal Processing
math.IT
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.
Cite
@article{arxiv.1806.10976,
title = {Sparse Sampling for Inverse Problems with Tensors},
author = {Guillermo Ortiz-Jiménez and Mario Coutino and Sundeep Prabhakar Chepuri and Geert Leus},
journal= {arXiv preprint arXiv:1806.10976},
year = {2019}
}
Comments
13 pages, 7 figures