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Sparse Sampling for Inverse Problems With Tensors

Ortiz-Jimenez, Guillermo and Coutino, Mario and Chepuri, Sundeep Prabhakar and Leus, Geert (2019) Sparse Sampling for Inverse Problems With Tensors. In: IEEE TRANSACTIONS ON SIGNAL PROCESSING, 67 (12). pp. 3272-3286.

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Official URL: https://doi.org/10.1109/TSP.2019.2914879

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.

Item Type: Journal Article
Additional Information: copyright for this article belongs to IEEE TRANSACTIONS ON SIGNAL PROCESSING
Keywords: Graph signal processing; multidimensional sampling; sparse sampling; submodular optimization; tensors
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 27 Jun 2019 14:59
Last Modified: 27 Jun 2019 14:59
URI: http://eprints.iisc.ac.in/id/eprint/63031

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