https://github.com/yokoxue/hrp
Code for paper "Efficient Sparse Coding using Hierarchical Riemannian Pursuit," in IEEE Transactions on Signal Processing, Y. Xue, V. K. N. Lau and S. Cai, doi: 10.1109/TSP.2021.3093769.
https://github.com/yokoxue/hrp
dictionary-learning learning-theory sample-complexity sparse-coding
Last synced: 6 months ago
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Code for paper "Efficient Sparse Coding using Hierarchical Riemannian Pursuit," in IEEE Transactions on Signal Processing, Y. Xue, V. K. N. Lau and S. Cai, doi: 10.1109/TSP.2021.3093769.
- Host: GitHub
- URL: https://github.com/yokoxue/hrp
- Owner: yokoxue
- Created: 2021-04-07T02:47:29.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-07-20T06:35:50.000Z (over 4 years ago)
- Last Synced: 2025-03-30T23:01:35.759Z (8 months ago)
- Topics: dictionary-learning, learning-theory, sample-complexity, sparse-coding
- Language: MATLAB
- Homepage:
- Size: 14.5 MB
- Stars: 13
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# HRP
Code for paper "Efficient Sparse Coding using Hierarchical Riemannian Pursuit," in IEEE Transactions on Signal Processing, Y. Xue, V. K. N. Lau and S. Cai, doi: 10.1109/TSP.2021.3093769.[[paper]](https://arxiv.org/pdf/2104.10314.pdf)
If you find they are useful, please cite:
```
@ARTICLE{9470930,
author={Xue, Ye and Lau, Vincent K. N. and Cai, Songfu},
journal={IEEE Transactions on Signal Processing},
title={Efficient Sparse Coding using Hierarchical Riemannian Pursuit},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TSP.2021.3093769}}
```
## 1.0 Prerequisites
+ **Matlab**
+ **KSVD Matlab toolbox （for Baseline 1)**
Download KSVD v13 from https://www.cs.technion.ac.il/~ronrubin/software.html and install
(OMP-Box v10 is required).
+ **SPAMS Matlab toolbox v2.6 （for Baseline 2)**
Download SPAMS from http://spams-devel.gforge.inria.fr/downloads.html.
Follow the steps in https://github.com/xhm1014/spams-matlab-install-on-win10 to install.
+ **CVX Matlab toolbox （for Baseline 4)**
Download CVX toobox from http://cvxr.com/cvx/ and install.
## 2.0 Generate the results for the convergence curves
Run `Converge_sim.m` in the folder `curve_convergence`.
## 3.0 Generate the results for the sample complexity curves
Run `Sample_sim.m` in the folder `curve_samplecomplexity`.
## 4.0 Generate the results for the RMSE heatmap with synthetic data
+ Unzip the .zip files in the folder `heatmap_synthetic`.
+ Run `Syndata_main.m` in the folder `heatmap_synthetic`.
## 5.0 Generate the results for the table with real-world sensor data
+ Unzip all the .zip files in the folder `table_sensor`.
+ Run `Sensor_Data_main.m` in the folder `table_sensor`.
+ 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.