Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
Authors: Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results and Analysis |
| Researcher Affiliation | Academia | Zhao Zhang1, Weiming Jiang2, Zheng Zhang3, Sheng Li4, Guangcan Liu5 and Jie Qin6 1 School of Computer Science & School of Artificial Intelligence, Hefei University of Technology, China 2 School of Computer Science and Technology, Soochow University, China 3 School of Information Technology and Electrical Engineering, University of Queensland, Australia 4 Department of Computer Science, University of Georgia, USA 5 School of Information and Control, Nanjing University of Information Science and Technology, China 6 Computer Vision Laboratory, ETH Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 Scalable Locality-Constrained Projective DL |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MIT CBCL Face Database [Weyrauch et al., 2004], AR Face Database [Martinez and Benavente, 1998], Caltech101 Fatabase[Perona et al., 2004], Caltech256 Database [Griffin et al., 2007], Yale face database (at http://vision.csd.edu/content/yale-face-database) |
| Dataset Splits | No | The paper describes training and testing splits (e.g., 'randomly select 4 images per person for training, while test on the rest.') but does not explicitly mention a distinct validation set or its split. |
| Hardware Specification | Yes | We perform all the simulations on a PC with Intel (R) Core (TM) i3-4130 CPU @ 3.4 GHz 8G. |
| Software Dependencies | No | The paper mentions using PCA and LDA for feature reduction but does not specify any software libraries or their version numbers used for implementation. |
| Experiment Setup | Yes | Parameters =0.01, τ =0.01 α and =0.1 β is used in our LC-PDL. Parameters =0.01, τ =0.1 α and =0.1 β are set for LC-PDL. LC-PDL works well in a wide range of parameters α and β , which means our LC-PDL model is insensitive to the parameters α and β by delivering stable performances. It is also noted that that a larger τ than 10-2 tend to decrease the recognition result, i.e., a small τ can be used. |