Robust Principal Component Analysis with Adaptive Neighbors
Authors: Rui Zhang, Hanghang Tong
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiment Diverse experiments are conducted to evaluate the performance of our method. Firstly, the experimental settings are provided. Moreover, the experimental results on different tasks are recorded. |
| Researcher Affiliation | Academia | Rui Zhang Arizona State University Tempe, AZ, U.S.A. ruizhang8633@gmail.com Hanghang Tong University of Illinois at Urbana-Champaign Urbana, Illinois, U.S.A. htong@illinois.edu |
| Pseudocode | Yes | Algorithm 1: Algorithm for solving RWL-AN in (6) |
| Open Source Code | No | The paper does not provide any specific repository link or explicit statement about the public availability of the source code for the described methodology. |
| Open Datasets | Yes | Four benchmark face image datasets including AT&T [1], UMIST [3], FEI and FERET [12] are utilized in the experiment. Table 1 reports the information for the benchmark datasets. |
| Dataset Splits | No | The paper mentions running experiments '50 times with random initialization' and uses a 'noise rate' for the datasets, but it does not specify explicit training/validation/test splits or cross-validation setup for reproducing the data partitioning. |
| Hardware Specification | Yes | Note that all the experiments are implemented by MATLAB R2015b on Windows 7 PC with 3.20 GHz i5-3470 CPU and 16.0 GB main memory. |
| Software Dependencies | Yes | Note that all the experiments are implemented by MATLAB R2015b on Windows 7 PC... |
| Experiment Setup | Yes | The integer parameter k of our method is setted as [0.85N] (N is the total number of data points), such that 85% samples are assigned with non-zero weights. As for capped R2DPCA, ϵ is searched in the grid of {10, 20, ..., 50} and the best results are recorded. |