Analysis of Robust PCA via Local Incoherence
Authors: Huishuai Zhang, Yi Zhou, Yingbin Liang
NeurIPS 2015 | Venue 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 EMAIL Yi Zhou Department of EECS Syracuse University Syracuse, NY 13244 EMAIL Yingbin Liang Department of EECS Syracuse University Syracuse, NY 13244 EMAIL |
| 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. |