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..
Residual-Based Sampling for Online Outlier-Robust PCA
Authors: Tianhao Zhu, Jie Shen
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report some numerical results on synthetic data. Our goal is to illustrate the properties of the online robust algorithm discussed in section 1, and compare our residual-based sampling for online robust PCA algorithm with the algorithm in Feng et al. (2013a)...Simulation results for optimum low rank k = 5 with different number of outliers z = 100, 150, 200 have been shown in Figure 1.Table 2. Comparison for the embedding dimension, marked outliers and execution time of our algorithm and Feng et al. (2013a) when d = 500, k = 5, z = 100. |
| Researcher Affiliation | Academia | Tianhao Zhu 1 Jie Shen 1 1Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA. Correspondence to: Tianhao Zhu <EMAIL>, Jie Shen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Online ORPCA with Logarithmic Approximation Error |
| Open Source Code | No | The paper does not include an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states: 'In this section, we report some numerical results on synthetic data. Our goal is to illustrate the properties of the online robust algorithm... To make a fair comparison, we simulate the contaminated data as follows. We randomly generate an d k matrix A...' |
| Dataset Splits | No | The paper describes experiments on 'synthetic data' and presents 'Simulation results' but does not explicitly mention or specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | To make a fair comparison, we simulate the contaminated data as follows. We randomly generate an d k matrix A, and scale it to make its magnitudes of the leading eigenvalues = 2. Then we multiple A with another uniformly generated matrix X = Rk n to make L = AX. A fraction λ of outliers are generated with uniform distribution over [ 20, 20], where z = λn is the number of outliers.Simulation results for optimum low rank k = 5 with different number of outliers z = 100, 150, 200 have been shown in Figure 1. |