Avoiding Optimal Mean Robust PCA
Authors: ℓ
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some experimental results on benchmark data sets demonstrate the effectiveness and superiority of the proposed approaches on image reconstruction and recognition. |
| Researcher Affiliation | Academia | 1 Shaanxi Province Key Lab of Satellite-Terrestrial Network , Department of Computer Science, Xi an Jiaotong University, P. R. China. 2 School of Computer Science and Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, P. R. China. 3Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney. 4School of Computer Science, Carnegie Mellon University, PA, USA |
| Pseudocode | Yes | Algorithm 1 Non-greedy 1-norm maximization. |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code for the described methodology. |
| Open Datasets | Yes | Specifically, the reconstruction errors with respect to nine different reduced dimensions from 10 to 50 are reported over 5 benchmark data sets, including the Japanese Female Facial Expression Database (JAFFE) [Dailey et al., 2010], UMIST face data set [Wechsler et al., 2012], the ORL database of faces [Cai et al., 2007], Columbia Object Image Library-20 (COIL-20) data set [Nene et al., 1996] and the USPS handwritten digit database [Liu et al., 2003]. All of the image data sets are downloaded from different web sites. |
| Dataset Splits | Yes | We randomly select half of the images from each object to form the training set and retain the rest as testing set for both of the data sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | randomly select 20% images to be occluded with randomly place of 1/4 size for fair comparison. We randomly select half of the images from each object to form the training set and retain the rest as testing set for both of the data sets. To illustrate the robustness of 2DRPCA-AOM, we corrupt a varying percentage of training images with outliers and recognize testing face images in the reduced space with the nearest neighbor (NN) classifier. |