Hyperplane Clustering via Dual Principal Component Pursuit
Authors: Manolis C. Tsakiris, René Vidal
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data, are shown to outperform or be competitive to the state-of-the-art. 5. Experimental Evaluation. |
| Researcher Affiliation | Academia | Manolis C. Tsakiris 1 Ren e Vidal 1 1Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA. |
| Pseudocode | No | The paper describes algorithms in prose, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In this section we explore various hyperplane clustering algorithms using the benchmark dataset NYUdepth V2 (Silberman et al., 2012). |
| Dataset Splits | No | The paper mentions evaluating algorithms on 'manually annotated 92 of the 1449 scenes' from the NYUdepth V2 dataset, but does not specify train, validation, or test dataset splits. |
| Hardware Specification | Yes | a MATLAB implementation on a standard Mac Book Pro with a dual core 2.5GHz processor and a total of 4GB cache memory |
| Software Dependencies | No | The paper mentions 'a MATLAB implementation' and 'an optimized LP solver such as GUROBI' but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | DPCP-r, which uses a maximum of 20 iterations in (3), while REAPER and DPCP-IRLS use a maximum of 100 iterations and convergence accuracy 10 3. We use 10 independent restarts. |