R. Varma*, H. Lee*, J. Kovačević and Y. Chi, “Vector-Valued Graph Trend Filtering With Non-Convex Penalties,” in IEEE Transactions on Signal and Information Processing over Networks, vol. 6, pp. 48-62, 2020.
https://ieeexplore.ieee.org/document/8926407
You can view the paper on arXiv https://arxiv.org/abs/1905.12692.
Note that when you run these scripts, numbers can be different from what is reported in the paper due to random noise.
* Figure 4
script/support_recovery.ipynb
* Figure 5
middle panel: script(3)/script_denoising_simulation.py
right panel: script3/Runtime experiment.ipynb
datasets/2d-grid
datasets/minnesota
* Table 3
script(3)/script_diff_snr_measurements.py
* Table 4
script(3)/script_UCI_clean_data.py
script(3)/script_UCI_classification.py
script(3)/script_UCI_get_pvalues.py
datasets/UCI_data
* Figure 6
script3/NYC_taxi.ipynb
* All other figures
script/plotting.ipynb
GTF/ and scripts/ are developed in Python 2.7, and
* Numpy 1.11.3
* Scipy 1.1.0
* Pandas 0.23.4
* Matplotlib 2.2.3
* NetworkX 2.1
* Hyperopt 0.2
* PyGSP 0.5.1
GTF3/ and scripts3/ are developed in Python 3.6, and
* Numpy 1.17.0
* Scipy 1.1.0
* Pandas 0.24.2
* Matplotlib 3.0.1
* NetworkX 2.3
* Hyperopt 0.2
* PyGSP 0.5.1
* tqdm 4.26.0