Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery
Authors: Mostafa Rahmani, George Atia
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <mostafa@knights.ucf.edu>. |
| Pseudocode | Yes | Algorithm 1 Co P: Proposed Robust PCA Algorithm, Algorithm 2 Adaptive Column Sampling for the Subspace Identiļ¬cation 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. |