Multi-View Clustering in Latent Embedding Space
Authors: Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang3513-3520
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach. In this section, extensive experiments are conducted to validate the superiority of the proposed method. |
| Researcher Affiliation | Academia | Man-Sheng Chen,1,2 Ling Huang,1,2 Chang-Dong Wang,1,2 Dong Huang3 1School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China 2Guangdong Province Key Laboratory of Computational Science, Guangzhou, China 3College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China |
| Pseudocode | Yes | Algorithm 1 MCLES Input: Multi-view matrices X = X(1), ..., X(V ) , cluster number c, parameters α, β and γ, and the embedding dimension d of latent representation. Initialize: W = 0, S = 0 and r = 1; Initialize H and P with random values. 1: repeat 2: repeat 3: r r + 1; 4: Update W according to Eq. (12). 5: until convergence 6: Update H according to Eq. (14). 7: For each i, update the i-th column of S by solving the problem in Eq. (18). 8: Update P, which is formed by the c eigenvectors of Ls = D ST +S 2 corresponding to the c smallest eigenvalues. 9: until convergence Output: W, H, S and P. |
| Open Source Code | Yes | The code of our method is available on the github1. 1https://github.com/Ttuo123/MCLES |
| Open Datasets | Yes | Datasets Description Yale2: It is a widely used face image dataset consisting of 165 gray-scale images belonging to 15 distinct subjects... 2http://cvc.yale.edu/projects/yalefaces/yalefaces.html. MSRCv1 (Winn and Jojic 2005): It is an image dataset... ORL3: It is a widely used face image dataset... 3http://www.cl.cam.ac.uk/research/dtg/. BBCSport (Xia et al. 2014): It is a document dataset... |
| Dataset Splits | No | The paper describes parameter analysis and running experiments multiple times ('run 20 times for each experiment'), but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) or a detailed cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions algorithms used (e.g., ADMM) and general tools ('Many existing quadratic programming packages'), but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Table 1: The default values of the four parameters. Parameter Yale MSRCv1 ORL BBCSport d 30 70 50 40 α 0.8 0.8 0.8 0.8 β 0.4 0.4 0.5 2 γ 0.004 0.004 0.004 0.004 |