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Blind Demixing of Sparse Signals Diffused on Graphs

MATLAB solver for blind separation of sparse signals diffused on graphs. [1]

Diffusion of signals defined on the nodes of a graph is a generalization of the convolution from classical signal processing. [2] [3]

[1] F. J. Iglesias, S. Segarra, S. Rey-Escudero, A. G. Marques and D. Ramírez, Demixing and Blind Deconvolution of Graph-Diffused Sparse Signals. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary (2018) (check in Google scholar).

[2] D. I. Shuman, et al., The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE signal processing magazine (2013).

[3] G. Mateos, et al., Connecting the Dots: Identifying Network Structure via Graph Signal Processing. IEEE signal processing magazine (2019).

ICASSP 2018

Scenario 1. Single random graph (multiple diffusing filters on the same graph).

>> addpath experiment/experiment_singlegraph_bss_logdet
>> singlegraph_bss_logdet_N_S_numFilters
>> % Long for 1000 simulations!
>> % Alternatively, there are pre-generated mat files
>> % in experiment/experiment_singlegraph_bss_logdet.

>> % Crunch data from mat files and produce mean and median RMSE plots.
>> analysis_singlegraph_bss_logdet

Scenario 2. Two coupled random graphs.

TODO

Scenario 3. Multiple brain graphs.

>> addpath experiment/experiment_brain_bss_logdet
>> brain_bss_logdet_S_numGraphs
>> % Very long for 1000 simulations!
>> % Pre-generated mat files in experiment/experiment_brain_bss_logdet.

>> % Produce plot.
>> brain_bss_logdet_S_numGraphs_figure