Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
Authors: Junhui Zhang, Jingkai Yan, John Wright
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our new method through experiments on simulated and real datasets. Our simulations corroborate the claim that a universal choice of the regularization parameter yields near optimal performance across a range of noise levels, indicating that the proposed method outperforms the (somewhat loose) bound proved here. |
| Researcher Affiliation | Academia | Junhui Zhang Department of Applied Physics and Applied Math Columbia University New York, NY 10027 jz2903@columbia.edu Jingkai Yan Department of Electrical Engineering Columbia University New York, NY 10027 jy2927@columbia.edu John Wright Department of Electrical Engineering Columbia University New York, NY 10027 jw2966@columbia.edu |
| Pseudocode | Yes | Algorithm 1 Algorithm for PCP |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use the hall dataset in [30]... We use the Dark Raw Video (DRV) dataset in [32] (under MIT License)... |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits (e.g., percentages or absolute counts). |
| Hardware Specification | Yes | We run the experiments on a laptop with 2.3 GHz Dual-Core Intel Core i5 |
| Software Dependencies | No | The paper mentions using 'Matlab' and functions like 'rgb2gray()' and 'imadjustn()', but no specific version numbers are provided for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For the added noise, we choose σ {0, 30, 60, 90, 120}... We take λ = 1/ n1, µroot = p n2/2 and µstable = 1 σ( n1+ n2)... set the maximal iteration of ADMM to be 5000. |