Learning Feature Sparse Principal Subspace
Authors: Lai Tian, Feiping Nie, Rong Wang, Xuelong Li
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the promising performance and efficiency of the new algorithms compared with the state-of-the-arts on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Lai Tian School of Computer Science & Center for OPTIMAL, Northwestern Polytechnical University, Xi an 710072, China. tianlai.cs@gmail.com Feiping Nie School of Computer Science & Center for OPTIMAL, Northwestern Polytechnical University, Xi an 710072, China. feipingnie@gmail.com Rong Wang School of Cybersecurity & Center for OPTIMAL, Northwestern Polytechnical University, Xi an 710072, China. wangrong07@tsinghua.org.cn Xuelong Li School of Computer Science & Center for OPTIMAL, Northwestern Polytechnical University, Xi an 710072, China. li@nwpu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Go for rank(A) m |
| Open Source Code | No | No explicit statement or link providing concrete access to source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We consider real-world datasets, including Lymphoma (biology) [48], NUS-WIDE (web images) [10], and Numerical Numbers (handwritten numbers) [3]. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. It mentions repeated runs and fixed parameters for synthetic data. |
| Hardware Specification | Yes | All experiments in this paper were run on MATLAB 2018a with a 2.3 GHz Quad-Core Intel Core i5 CPU and 16GB memory MBP. |
| Software Dependencies | Yes | All experiments in this paper were run on MATLAB 2018a with a 2.3 GHz Quad-Core Intel Core i5 CPU and 16GB memory MBP. |
| Experiment Setup | Yes | For the synthetic data, we fix m = 3, k = 7,and d = 20. We cannot afford large-scale setting since the brute-force searching space grows exponentially. We consider three different initialization methods: Random Subspace; Convex Relaxation proposed in [41] and used in [43]; Low Rank Approx. with GO(Am, m, k, d). In our experiments, we always use Aε with ε = 0.1 to keep safe (Remark 5.10). |