Self-Paced Two-dimensional PCA
Authors: Jiangxin Li, Zhao Kang, Chong Peng, Wenyu Chen8392-8400
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on image reconstruction and clustering verify the superiority of our approach. Four benchmark image databases, including ORL, MNIST, AR and EYale B, are utilized in the experiments. |
| Researcher Affiliation | Academia | Jiangxin Li,1 Zhao Kang, 1 Chong Peng, 2 Wenyu Chen1 1 School of Computer Science and Engineering, University of Electronic Science and Technology of China 2 College of Computer Science and Technology, Qingdao University |
| Pseudocode | Yes | Algorithm 1 SP2DPCA |
| Open Source Code | Yes | The code of our implementation is published 1. 1https://github.com/sckangz/SP2DPCA. |
| Open Datasets | Yes | Four benchmark image databases, including ORL, MNIST, AR and EYale B, are utilized in the experiments. |
| Dataset Splits | No | Following the comparison methods, half of the images are used for training and the rest is left for testing. The paper mentions train and test splits but does not specify a separate validation split. |
| Hardware Specification | No | The paper mentions running times but does not provide specific hardware details (e.g., CPU/GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using 'k-means clustering algorithm' but does not provide version numbers for any software dependencies. |
| Experiment Setup | Yes | For SP2DPCA, the optimal parameters are searched in the range ΞΆ = {50, 100, 200, 500, 1000} and c = {300, 500, 1000, 3000, 5000} and the best results are recorded correspondingly. We stop the algorithm when the loss value does not change much. |