Block Sparse Bayesian Learning: A Diversified Scheme
Authors: Yanhao Zhang, Zhihan Zhu, Yong Xia
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the advantages of Div SBL over existing algorithms. In this section, we compare Div SBL with the following six algorithms:4 1. Block-based algorithms: (1) BSBL, (2) Group Lasso, (3) Group BPDN. 2. Pattern-based algorithms: (4) PC-SBL, (5) Struct OMP. 3. Sparse learning (without structural information): (6) SBL. Results are averaged over 100 or 500 random runs (based on computational scale), with SNR ranging from 15-25 d B except the test for varied noise levels. Normalized Mean Squared Error (NMSE) , defined as ||ˆx xtrue||2 2/||xtrue||2 2, and Correlation (Corr) (cosine similarity) are used to compare algorithms. |
| Researcher Affiliation | Academia | Yanhao Zhang Zhihan Zhu Yong Xia School of Mathematical Sciences, Beihang University Beijing, 100191 {yanhaozhang, zhihanzhu, yxia}@buaa.edu.cn |
| Pseudocode | Yes | In conclusion, the Diversified SBL (Div SBL) algorithm is summarized as Algorithm 1 below. The procedure, using dual ascent method to diversify Bi, is summarized in Algorithm 2 as follows: |
| Open Source Code | Yes | Matlab codes for our algorithm are available at https://github.com/Yanhao Zhang1/Div SBL . |
| Open Datasets | Yes | We initially test on synthetic signal data, including homoscedastic (provided by [24]) and heteroscedastic data... randomly chosen in Audio Set [34]... In 2D image experiments, we utilize a standard set of grayscale images compiled from two sources 6. Available at http://dsp.rice.edu/software/DAMP-toolbox and http://see.xidian.edu.cn/faculty/wsdong/NLR_Exps.htm |
| Dataset Splits | No | The paper does not explicitly provide details about a validation dataset split (e.g., percentages, sample counts, or methodology for a dedicated validation set). |
| Hardware Specification | No | The paper mentions 'CPU time' in Appendix D but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | Matlab codes for our algorithm are available at https://github.com/Yanhao Zhang1/Div SBL . While implying the use of Matlab, the paper does not specify version numbers for Matlab or any other key software components used in the experiments. |
| Experiment Setup | Yes | Results are averaged over 100 or 500 random runs (based on computational scale), with SNR ranging from 15-25 d B except the test for varied noise levels. The sensitivity to initialization on the heteroscedastic signal from Section 5.1. Initial variances are set to γ = η ones(g L, 1) and γ = η rand(g L, 1) with the scale parameter η ranging from 1 10 1 to 1 104. Input: Measurement matrix Φ, response y, initialized variance γ, prior s covariance Σ0, noise s variance β, and multipliers λ0. |