Efficient Algorithms for Robust One-bit Compressive Sensing
Authors: Lijun Zhang, Jinfeng Yi, Rong Jin
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform the recovery experiment to verify our theoretical claims. |
| Researcher Affiliation | Collaboration | Lijun Zhang ZHANGLJ@LAMDA.NJU.EDU.CN National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China Jinfeng Yi JINFENGY@US.IBM.COM IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA Rong Jin RONGJIN@CSE.MSU.EDU Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA |
| Pseudocode | Yes | Algorithm 1 An adaptive algorithm for One-bit Compressive Sensing |
| Open Source Code | No | The paper provides a link for a baseline algorithm ('A matlab implementation can be downloaded from http://perso.uclouvain.be/laurent.jacques/ index.php/Main/BIHTDemo.'), but does not provide specific access to the source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('We generate the target vector x Rn by drawing its nonzero elements from the standard Gaussian distribution...'), rather than using a publicly available or open dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating synthetic data and running experiments based on varying parameters (m, n, s) and repeating trials, but does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'CVX package' and 'A matlab implementation' (for a baseline), but does not provide specific version numbers for these software dependencies (e.g., 'CVX 2.0' or 'Matlab R2020a') to ensure reproducibility. |
| Experiment Setup | Yes | From the result, we observe that the best value of C is around 1, and thus we set γ = q m in the following experiments. |