Reconstruction Weighting Principal Component Analysis with Fusion Contrastive Learning
Authors: Qianqian Wang, Meiling Liu, Wei Feng, Mengping Jiang, Haiming Xu, Quanxue Gao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on six datasets to evaluate the performance of our method, which indicates our method greatly improves the classification accuracy and demonstrates its effectiveness in capturing more information about sample differences.4 Experiment |
| Researcher Affiliation | Academia | 1School of Telecommunications Engineering, Xidian University, Xi an, China 2Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, China, 210096. 3School of Computer Science and Technology, Xi an Jiaotong University, Xi an, China |
| Pseudocode | No | No explicit pseudocode or algorithm block is present in the main body of the paper. Section 3.3 mentions 'The detailed optimization can be found in the Supplementary material'. |
| Open Source Code | Yes | 1https://github.com/lml314159/IJCAI2024 |
| Open Datasets | Yes | Extended Yale B:[Georghiades et al., 2001] The Yale B dataset comprises 5850 facial images from 10 individuals, later expanded and renamed the Extended Yale B dataset. It encompasses 2144 frontal images of 38 subjects captured under diverse lighting setups. ... CMU PIE:[Sim et al., 2002] The CMU PIE database was created by Carnegie Mellon University. ... AR: [Martinez and Benavente, 1998] The AR dataset comprises 4,000 color facial images from 56 females and 70 males... ORL:[Jin et al., 2001] The ORL dataset includes 400 grayscale frontal facial images of 40 subjects... Lung:[Li et al., 2018] The Lung dataset constitutes a biological repository comprising 203 samples categorized into five distinct classes. |
| Dataset Splits | No | The paper explicitly describes training and testing splits for all datasets, e.g., for Extended Yale B: 'with 7 noisy images assigned for training, totaling 1216 images. The remaining 1198 images were allocated for testing purposes.' However, no explicit validation dataset split is mentioned. |
| Hardware Specification | No | The paper states 'All our experiments were conducted on the Windows 10 operating system using Python 3.7.', but does not provide any specific hardware details such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'Python 3.7' for implementation but does not specify other key software dependencies or libraries with their version numbers, which are necessary for full reproducibility. |
| Experiment Setup | Yes | Furthermore, for fairness, we run each method for 10,000 epochs and set the algorithm parameters in accordance with the specifications in their papers.When the hyperparameter η is fixed as 0.8, the recognition rate under each dimension reaches its highest value when the weight λ of contrastive learning is set to around 0.0001. ... When fixing λ to 0.0001, it is found that the recognition accuracy under each dimension reaches its highest value when η is set to around 0.8... |