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..
Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery
Authors: Mostafa Rahmani, George Atia
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Numerical Simulations In this section, the performance of the proposed method is investigated with both synthetic and real data. We compare the performance of Co P with the state-of-the-art robust PCA algorithms including FMS (Lerman & Maunu, 2014), GMS (Zhang & Lerman, 2014), R1-PCA (Ding et al., 2006), OP (Xu et al., 2010b), and SPCA (Maronna et al., 2006)., Table 1. Running time of the algorithms m = n Co P FMS OP R1-PCA 1000 0.02 1 45 1.45 2000 0.7 5.6 133 10.3 5000 5.6 60 811 83.3 10000 27 401 3547 598 |
| Researcher Affiliation | Academia | 1University of Central Florida, Orlando, Florida, USA. Correspondence to: Mostafa Rahmani <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Co P: Proposed Robust PCA Algorithm, Algorithm 2 Adaptive Column Sampling for the Subspace Identification Step (step 3) of Co P, Algorithm 3 Subspace Clustering Error Correction Method |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use the Hopkins155 dataset which contains video sequences of 2 or 3 motions (Tron & Vidal, 2007). |
| Dataset Splits | No | The paper describes parameters for synthetic data generation (e.g., 'm = 100, r = 10', 'n1 = n/5 and n2 = 4n/5') and uses the Hopkins155 dataset, but it does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits for validation) needed for reproduction. |
| Hardware Specification | No | The paper provides running times in Table 1 but does not specify any concrete hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 1 Co P: Proposed Robust PCA Algorithm Initialization: Set p = 1 or p = 2., For instance, consider a setting in which m = 400, r = 5, n1 = 50, and the distributions of inliers and outliers follow Assumption 1. Fig. 1 shows the vector p (c.f. Algorithm 1) for different values of p and n2., In this simulation, we construct the matrix Y using 20 columns of X corresponding to the largest 20 elements of p., In this simulation, we use 30 columns to form the matrix Y., When Co P is applied, 50 percent of the columns of X are used to form the matrix Y. |