Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hyperplane Clustering via Dual Principal Component Pursuit
Authors: Manolis C. Tsakiris, René Vidal
ICML 2017 | Venue PDF | 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. |