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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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
Publication:	IEEE TRANSACTIONS ON SIGNAL PROCESSING
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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
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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