Robust Out-of-Sample Data Recovery
Authors: Bo Jiang, Chris Ding, Bin Luo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six image datasets demonstrate the effectiveness and benefits of the proposed ROSR method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Anhui University, Hefei, 230601, China 2CSE Department, University of Texas at Arlington, Arlington, TX 76019, USA |
| Pseudocode | Yes | Algorithm 1 Robust Out-of-Sample Recovery |
| Open Source Code | No | The paper does not provide any statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | Six image datasets are used in the experiments, including three face datasets (AT&T face databases, Extended Yale Database B [Lee et al., 2005] and CMU-PIE [He et al., 2005a]) and three handwritten character datasets (Binary Alphabet Dataset 2, MNIST handwritten digits Database, USPS Handwritten Digits Dataset3). and footnotes 2http://olivier.chapelle.cc/ssl-book/benchmarks.html 3http://www.cs.nyu.edu/ roweis/data.html |
| Dataset Splits | Yes | All experiments are performed with ten-fold cross validation strategy, i.e., all data sets are randomly splitted into ten equal subsets, iteratively pick one subset for testing and the remaining nine subsets for training, then the performances are averaged over the ten loops. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory, or processing units). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We set the regularization parameter β in ROSR to 0.8λ0, 1.0λ0 and 1.2λ0, respectively, where λ0 = pp and p is the dimension of data. Note that λ = λ0 is used in RPCA model in the experiment. For PCA, LPP, NPE and L1PCA, we set the dimension d to 50 and 100, respectively. |