TY - JOUR
T1 - Sparse Sampling for Inverse Problems With Tensors
AU - Ortiz-Jimenez, Guillermo
AU - Coutino, Mario
AU - Chepuri, Sundeep Prabhakar
AU - Leus, Geert
N1 - KAUST Repository Item: Exported on 2021-03-12
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work was supported in part by the ASPIRE project (Project 14926 within the STW OTP programme), in part by the Netherlands Organization for Scientific Research, and in part by the KAUST-MIT-TUD consortium under Grant OSR-2015-Sensors-2700. The
work of G. Ortiz-Jiménez was supported by a fellowship from Fundación Bancaria “la Caixa.” The work of M. Coutino was supported by CONACYT. This paper was presented in part at the Sixth IEEE Global Conference on Signal and Information Processing, Anaheim, CA, November 2018 [1].
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2019/6/15
Y1 - 2019/6/15
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/668095
UR - https://ieeexplore.ieee.org/document/8705331/
UR - http://www.scopus.com/inward/record.url?scp=85066879521&partnerID=8YFLogxK
U2 - 10.1109/tsp.2019.2914879
DO - 10.1109/tsp.2019.2914879
M3 - Article
SN - 1053-587X
VL - 67
SP - 3272
EP - 3286
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 12
ER -