Contents I. Introduction
With the explosive growth of information and communication, signals are generated at an unprecedented rate from various sources, including social, citation, biological, and physical infrastructure [1], [2], among others.
Abstract:
We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling ...Show MoreMetadata
Abstract:
We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is “spectrum-blind” where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdös-Rényi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.
Published in: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Date of Conference: 13-16 December 2015
Date Added to IEEE Xplore: 21 January 2016
ISBN Information:
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Cites in Papers - |
Cites in Papers - IEEE (12)
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1.
Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro, "Convolutional Learning on Multigraphs", IEEE Transactions on Signal Processing, vol.71, pp.933-946, 2023.
2.
Fernando Gama, Antonio G. Marques, Alejandro Ribeiro, Geert Leus, "Aggregation Graph Neural Networks", ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4943-4947, 2019.
3.
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs", IEEE Transactions on Signal Processing, vol.67, no.4, pp.1034-1049, 2019.
4.
Diego Valsesia, Giulia Fracastoro, Enrico Magli, "Sampling of Graph Signals via Randomized Local Aggregations", IEEE Transactions on Signal and Information Processing over Networks, vol.5, no.2, pp.348-359, 2019.
5.
Fernando Gama, Elvin Isufi, Geert Leus, Alejandro Ribeiro, "Control of Graph Signals Over Random Time-Varying Graphs", 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4169-4173, 2018.
6.
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramírez, "Demixing and Blind Deconvolution of Graph-Diffused Sparse Signals", 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4189-4193, 2018.
7.
Rohan Varma, Siheng Chen, Jelena Kovačević, "Graph topology recovery for regular and irregular graphs", 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp.1-5, 2017.
8.
Sundeep Prabhakar Chepuri, Geert Leus, "Graph Sampling for Covariance Estimation", IEEE Transactions on Signal and Information Processing over Networks, vol.3, no.3, pp.451-466, 2017.
9.
David Ramírez, Antonio G. Marques, Santiago Segarra, "Graph-signal reconstruction and blind deconvolution for diffused sparse inputs", 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4104-4108, 2017.
10.
Luiz F. O. Chamon, Alejandro Ribeiro, "Universal bounds for the sampling of graph signals", 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3899-3903, 2017.
11.
Fernando Garna, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking sketching as sampling: Efficient approximate solution to linear inverse problems", 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp.390-394, 2016.
12.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking sketching as sampling: Linear transforms of graph signals", 2016 50th Asilomar Conference on Signals, Systems and Computers, pp.522-526, 2016.
Cites in Papers - Other Publishers (1)
1.
Jie Yang, Ce Shi, Yueyan Chu, Wenbin Guo, "Graph Signal Reconstruction Based on Spatio-temporal Features Learning", Digital Signal Processing, pp.104414, 2024.
