Type
ArticleKAUST Grant Number
OSR-2015-Sensors-2700Date
2019-06-15Permanent link to this record
http://hdl.handle.net/10754/668095
Metadata
Show full item recordAbstract
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.Citation
Ortiz-Jimenez, G., Coutino, M., Chepuri, S. P., & Leus, G. (2019). Sparse Sampling for Inverse Problems With Tensors. IEEE Transactions on Signal Processing, 67(12), 3272–3286. doi:10.1109/tsp.2019.2914879Sponsors
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].arXiv
1806.10976Additional Links
https://ieeexplore.ieee.org/document/8705331/ae974a485f413a2113503eed53cd6c53
10.1109/tsp.2019.2914879