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Xue, V. K. N. Lau and S. Cai, doi: 10.1109/TSP.2021.3093769.[[paper]](https://arxiv.org/pdf/2104.10314.pdf)\n\nIf you find they are useful, please cite:\n```\n@ARTICLE{9470930,\n  author={Xue, Ye and Lau, Vincent K. N. and Cai, Songfu},\n  journal={IEEE Transactions on Signal Processing}, \n  title={Efficient Sparse Coding using Hierarchical Riemannian Pursuit}, \n  year={2021},\n  volume={},\n  number={},\n  pages={1-1},\n  doi={10.1109/TSP.2021.3093769}}\n```\n## 1.0 Prerequisites\n+ **Matlab**\n\n+ **KSVD Matlab toolbox （for Baseline 1)**\n\nDownload KSVD v13 from https://www.cs.technion.ac.il/~ronrubin/software.html and install\n(OMP-Box v10 is required).\n\n+ **SPAMS Matlab toolbox v2.6 （for Baseline 2)**\n\nDownload SPAMS from  http://spams-devel.gforge.inria.fr/downloads.html.\nFollow the steps in https://github.com/xhm1014/spams-matlab-install-on-win10 to install.\n\n\n+ **CVX Matlab toolbox （for Baseline 4)**\n\nDownload CVX toobox from http://cvxr.com/cvx/ and install.\n\n## 2.0 Generate the results for the convergence curves\nRun   `Converge_sim.m` in the folder `curve_convergence`.\n\n## 3.0 Generate the results for the sample complexity curves\nRun  `Sample_sim.m` in the folder `curve_samplecomplexity`.\n\n## 4.0 Generate the results for the RMSE heatmap with synthetic data\n+ Unzip the .zip files in the folder `heatmap_synthetic`.\n+ Run `Syndata_main.m` in the folder `heatmap_synthetic`.\n\n## 5.0 Generate the results for the table with real-world sensor data\n+ Unzip all the .zip files in the folder `table_sensor`.\n+ Run `Sensor_Data_main.m` in the folder `table_sensor`.\n+ Raw data of the sensor readings of the Airly network can be downloaded from https://www.kaggle.com/datascienceairly/air-quality-data-from-extensive-network-of-sensors.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyokoxue%2Fhrp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyokoxue%2Fhrp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyokoxue%2Fhrp/lists"}