LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation
Authors: Zhengqin Xu, Yulun Zhang, Chao Ma, Yichao Yan, Zelin Peng, Shoulie Xie, Shiqian Wu, Xiaokang Yang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that LERE surpasses state-of-the-art (SOTA) methods. The code for this work is accessible at https://github.com/zhengqinxu/LERE. |
| Researcher Affiliation | Collaboration | 1AMo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China 2ETH Zürich, Switzerland 3Signal Processing, RF & Optical Dept. Institute for Infocomm Research A*STAR, Singapore 4School of Information Science and Engineering, Wuhan University of Science and Technology, China {fate311, chaoma, yanyichao, zelin.peng, xkyang}@sjtu.edu.cn, yulun100@gmail.com, slxie@i2r.a-star.edu.sg, shiqian.wu@wust.edu.cn |
| Pseudocode | Yes | Algorithm 1: LERE for RPCA |
| Open Source Code | Yes | The code for this work is accessible at https://github.com/zhengqinxu/LERE. |
| Open Datasets | Yes | Suite Sparse Database 1 which provides general data matrices with low numerical ranks, is tested, and these outcomes are depicted in Fig. 4. 1https://sparse.tamu.edu/ Furthermore, we also conduct evaluation on the video dataset from the Scene Background Initialization (SBI) datasets 2 and the Low Dynamic Range (LDR) datasets 3. 2https://sbmi2015.na.icar.cnr.it/SBIdataset.html 3http://alumni.soe.ucsc.edu/ orazio/deghost.html |
| Dataset Splits | No | The paper describes testing on synthetic and real-world datasets but does not explicitly mention any training/validation/test splits of a single dataset. It mentions 'training one set of parameters by the small sparsity dataset' but no specific split percentages or sample counts. |
| Hardware Specification | Yes | All of our tests run on a Windows 10 laptop with Intel i7-9750H CPU, 64G RAM. The parameters learning processes run on an Ubuntu workstation with Intel i9-9900K CPU and two Nvidia RTX-2080Ti GPUs. |
| Software Dependencies | No | The paper mentions employing 'Feedforward Recurrent-Mixed Neural Network (FRMNN) (Cai, Liu, and Yin 2021)' and 'RPCA' but does not specify version numbers for programming languages (e.g., Python), libraries (e.g., PyTorch, TensorFlow), or other software dependencies. |
| Experiment Setup | Yes | In our method, the sampling numbers are I = 4r log(n1) and J = 4r log(n2); the iteration number of FRMNN is 17. All parameters in compared methods follow their default settings. Moreover, all of our tests run on a Windows 10 laptop with Intel i7-9750H CPU, 64G RAM. The parameters learning processes run on an Ubuntu workstation with Intel i9-9900K CPU and two Nvidia RTX-2080Ti GPUs. We corrupt the input matrix Y Rn1 n2 by sparse noise with varying corruption rates α = {0.1, 0.3, 0.5} and parameters: n1 = 3000, n2 = {3000, 1000, 500}, rank r = 5; the iteration stop criterion is Y L S F / Y F < 10 7, where L and S are the reconstruction matrices; the maximum number of iterations is 200. |