A Pseudo-Bayesian Algorithm for Robust PCA
Authors: Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments To examine significant factors that influence the ability to solve (1), we first evaluate the relative performance of PB-RPCA estimating random simulated subspaces from corrupted measurements, the standard benchmark. Later we present subspace clustering results for motion segmentation as a practical application. |
| Researcher Affiliation | Collaboration | Tae-Hyun Oh1 Yasuyuki Matsushita2 In So Kweon1 David Wipf3 1Electrical Engineering, KAIST, Daejeon, South Korea 2Multimedia Engineering, Osaka University, Osaka, Japan 3Microsoft Research, Beijing, China |
| Pseudocode | No | the proposed pipeline, which we henceforth refer to as pseudo-Bayesian RPCA (PB-RPCA), involves the steps shown under Algorithm 1 in [23]. The pseudocode is in an external reference [23], not directly in this paper. |
| Open Source Code | No | The paper does not provide any explicit statements about code release or links to a source code repository. |
| Open Datasets | Yes | We adopt an experimental paradigm from [17] designed to test motion segmentation estimation in the presence of outliers. To mimic mismatches while retaining access to ground-truth, we randomly corrupt the entries of the trajectory matrix formed from Hopkins155 data [28]. |
| Dataset Splits | No | The paper describes using synthetic data and the Hopkins155 dataset for evaluation but does not specify explicit training, validation, or test splits, nor does it provide details on how the data was partitioned for these purposes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes algorithms and methods used (e.g., ADMM, IRLS) but does not provide specific details about software dependencies, such as programming languages, libraries, or solver names with their version numbers. |
| Experiment Setup | No | The paper states, 'Detailed settings and parameters can be found in [23],' which refers to an external arXiv paper. Therefore, this paper itself does not contain the specific experimental setup details or hyperparameters. |