Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
Authors: Chao Qu, Huan Xu
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish theoretical guarantees for the algorithm to identify the correct subspace, and demonstrate the effectiveness of the algorithm via numerical simulations. and 5 Numerical simulations |
| Researcher Affiliation | Academia | Chao Qu Department of Mechanical Engineering National University of Singapore A0117143@u.nus.edu Huan Xu Department of Mechanical Engineering National University of Singapore mpexuh@nus.edu.sg |
| Pseudocode | No | The paper describes the Robust Dantzig Selector using mathematical formulations, but it does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | The data used for numerical simulations are generated by the authors: 'In all experiments, the ambient dimension D = 200, sample density ρ = 5, the subspace are drawn uniformly at random. Each subspace has ρd+1 points chosen independently and uniformly random.' |
| Dataset Splits | No | The paper describes the parameters for synthetic data generation (e.g., 'ambient dimension D = 200, sample density ρ = 5'), but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes the parameters and setup for numerical simulations (e.g., 'ambient dimension D = 200, L = 3, d = 5'), but it does not specify any details regarding the hardware used to run these experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used to implement or run the experiments. |
| Experiment Setup | Yes | In all experiments, the ambient dimension D = 200, sample density ρ = 5, the subspace are drawn uniformly at random. Each subspace has ρd+1 points chosen independently and uniformly random. We first compare the robust Dantzig selector(λ = 2) with SSC and LASSO-SSC ( λ = 10). The results are shown in Figure 3. The X-axis is the number of irrelevant features and the Y-axis is the Relviolation defined above. The ambient dimension D = 200, L = 3, d = 5, the relative sample density ρ = 5. The values of irrelevant features are independently sampled from a uniform distribution in the region [ 2.5, 2.5] in (a) and [ 10, 10] in (b). |