https://github.com/iglesias/gsp_bss

Blind Separation of Sparse Signals Diffused on Graphs
https://github.com/iglesias/gsp_bss

blind-source-separation demixing graph-signal-processing network-science

Last synced: 24 days ago
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Blind Separation of Sparse Signals Diffused on Graphs

• Host: GitHub
• URL: https://github.com/iglesias/gsp_bss
• Owner: iglesias
• License: bsd-2-clause
• Created: 2017-05-15T07:25:00.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-01-13T18:00:04.000Z (12 months ago)
• Last Synced: 2024-10-15T22:48:36.983Z (2 months ago)
• Topics: blind-source-separation, demixing, graph-signal-processing, network-science
• Language: HTML
• Homepage: https://ieeexplore.ieee.org/document/8462154
• Size: 390 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://github.com/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](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=H0okuHUAAAAJ&citation_for_view=H0okuHUAAAAJ:_xSYboBqXhAC)).

[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).

```Matlab

>> 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.

```Matlab

>> 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

```