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arXiv · 1806.10976

Sparse Sampling for Inverse Problems with Tensors

Guillermo Ortiz-Jiménez·Mario Coutino·Sundeep Prabhakar Chepuri·Geert Leus
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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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Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus. 2018-06-28. Sparse Sampling for Inverse Problems with Tensors. https://doi.org/10.1109/tsp.2019.2914879

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