Analysis of Robust PCA via Local Incoherence
Authors: Huishuai Zhang, Yi Zhou, Yingbin Liang
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide numerical experiments to demonstrate our theoretical results. In these experiments, we adopt an augmented Lagrange multiplier algorithm in [17] to solve the PCP. We set λ = 1/pn log n. A trial of PCP (for a given realization of error locations) is declared to be successful if ˆL recovered by PCP satisfies kˆL Lk F /k Lk F 10 3. |
| Researcher Affiliation | Academia | Huishuai Zhang Department of EECS Syracuse University Syracuse, NY 13244 hzhan23@syr.edu Yi Zhou Department of EECS Syracuse University Syracuse, NY 13244 yzhou35@syr.edu Yingbin Liang Department of EECS Syracuse University Syracuse, NY 13244 yliang06@syr.edu |
| Pseudocode | No | The paper describes the steps of the proof and construction (e.g., Z0, Zk, RΓk Zk 1) but does not provide a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository. |
| Open Datasets | No | The paper does not use pre-existing public datasets. It describes generating low-rank matrices using "Bernoulli model", "Gaussian model", and "Cluster model" for simulations, but does not provide access information for these generated datasets. |
| Dataset Splits | No | The paper discusses performing "50 trials of independent error corruption" and judging success if "nine trials out of ten are successful" for a given (r, ρ) pair. However, it does not specify traditional training, validation, and test dataset splits with percentages or counts for a fixed dataset. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper mentions using "an augmented Lagrange multiplier algorithm in [17]" but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific solver versions). |
| Experiment Setup | Yes | In these experiments, we adopt an augmented Lagrange multiplier algorithm in [17] to solve the PCP. We set λ = 1/pn log n. A trial of PCP (for a given realization of error locations) is declared to be successful if ˆL recovered by PCP satisfies kˆL Lk F /k Lk F 10 3. For all three low rank matrix models, we set n = 1200 and rank r = 10. For each value of , we perform 50 trials of independent error corruption and count the number of failures of PCP. |