Discriminative Feature Selection via A Structured Sparse Subspace Learning Module
Authors: Zheng Wang, Feiping Nie, Lai Tian, Rong Wang, Xuelong Li
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on several high-dimensional datasets demonstrate the discriminability of selected features via S2DFS with comparison to several related SOTA feature selection methods. Source matlab code: https://github. com/Steven Wang NPU/L20-FS. Experimental results show the effectiveness of proposed optimization algorithm in two perspectives, i.e., performance: our method outperforms other related SOTA feature selection methods in terms of classification on several real-world datasets; convergent speed: our algorithm reaches convergence within few iterations. |
| Researcher Affiliation | Academia | 1School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi an, 710072, P. R. China 2School of Cybersecurity, Northwestern Polytechnical University, Xi an, 710072, P. R. China {zhengwangml, feipingnie, tianlai.cs}@gmail.com, wangrong07@tsinghua.org.cn, li@nwpu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Solve problem (2), when rank(A) > m ... Algorithm 2 Algorithm to solve the general maximization ratio problem (19). |
| Open Source Code | Yes | Source matlab code: https://github. com/Steven Wang NPU/L20-FS. |
| Open Datasets | Yes | We evaluate the performance of proposed method on several high-dimensional real-world datasets, and more details about them are shown in Table ??. For the color image datasets, i.e., Pubfig [Xu et al., 2018]... 1http://qwone.com/ jason/20Newsgroups/ 2http://www.cs.cmu.edu/afs/cs/project/theo-20/www/data/ 3http://www.escience.cn/system/file?file Id=82035 |
| Dataset Splits | No | The paper mentions using 'training data' and that 'all experiments are repeatedly conducted 10 times', but it does not specify any explicit train/validation/test splits or cross-validation setup for reproducing the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'matlab code' and using 'k-nearest neighbor algorithm as the classifier', but it does not specify any version numbers for Matlab or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | No | The paper mentions using a 'k-nearest neighbor algorithm as the classifier' and that 'all experiments are repeatedly conducted 10 times'. However, it does not provide specific hyperparameters for the classifier (e.g., value of k for kNN) or other detailed training configurations or system-level settings for the experiments. |