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Graph Learning from Data
License: GNU General Public License v2.0
This project forked from stac-usc/graph_learning
Graph Learning from Data
License: GNU General Public License v2.0
Graph Laplacian Learning (GLL) Package v2.2 This MATLAB package includes implementations of graph learning algorithms presented in [1]-[2]. The licensing information is available in LICENSE.txt [1] H. E. Egilmez, E. Pavez, and A. Ortega, "Graph learning from data under Laplacian and structural constraints," IEEE Journal of Selected Topics in Signal Processing, 2017. Arxiv version: H. E. Egilmez, E. Pavez, and A. Ortega, "Graph learning from data under structural and Laplacian constraints," CoRR, vol. abs/1611.05181v2,2016. [Online]. Available: https://arxiv.org/abs/1611.05181 [2] H. E. Egilmez, E. Pavez, and A. Ortega, "Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification," IEEE Transactions on Signal and Information Processing over Networks, 2018. Arxiv version: H. E. Egilmez, E. Pavez, and A. Ortega, "Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification," CoRR, vol. abs/1803.02553,2018. [Online]. Available: https://arxiv.org/abs/1803.02553 To install the package: (1) Download the source files. (2) Run the script 'start_graph_learning.m' The demo script 'demo_animals.m' shows the usage of functions used to estimate three different graph Laplacian matrices discussed in [1]. The demo script 'demo_us_temperature.m' shows the usage of functions used to estimate combinatorial Laplacian matrices from smooth signals discussed in [2]. The code regenerates Fig.7(e) in [2]. The demo script 'demo_artificial_data_on_grid.m' implements the following steps: - generates a grid graph with random edges and a $\beta$-hop filter - generates an artificial dataset based on the graph system, which is specified by the generated graph and filter - runs the iterative algorithm described in [2]. - shows ground truth and estimated graphs as well as returns the estimated $\beta$
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