Robust Auto-Weighted Multi-View Clustering
Authors: Pengzhen Ren, Yun Xiao, Pengfei Xu, Jun Guo, Xiaojiang Chen, Xin Wang, Dingyi Fang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to confirm the superiority and robustness of the proposed algorithm. |
| Researcher Affiliation | Academia | Pengzhen Ren, Yun Xiao , Pengfei Xu, Jun Guo, Xiaojiang Chen, Xin Wang, Dingyi Fang School of Information Science and Technology, Northwest University, Xi an 710127, P.R. China pengzhenr@stumail.nwu.edu.cn, yxiao@nwu.edu.cn, xpfu@nwu.edu.cn, guojun@nwu.edu.cn, xjchen@nwu.edu.cn, xinwang@nwu.edu.cn, dyf@nwu.edu.cn |
| Pseudocode | Yes | Algorithm 1 The algorithm of Robust Auto-Weighted multi-view Clustering (RAMC) in Eq. (4) |
| Open Source Code | No | The paper does not provide any specific link or statement regarding the availability of its source code. |
| Open Datasets | Yes | Following [Li et al., 2015], three widely used real-world multi-view datasets, MSRCv1 [Winn and Jojic, 2005], Caltech101 [Li et al., 2007] (following [Nie et al., 2017], two regular subsets Caltech101-7 and Caltech101-20 are used in our experiments) and Digits [Asuncion and Newman, 2007] are considered in the experiments. |
| Dataset Splits | No | The paper mentions using k-nearest neighbor method and setting parameter 'kappa', and evaluates performance using Purity and NMI, but it does not explicitly describe a separate validation dataset split or a cross-validation setup for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Only one parameter κ needs to be set in this method, here the κ represents the number of neighbor nodes. ... In this paper, κ is fixed to 16. ... The clustering Purity and NMI are shown in solid line and dotted line with step size 0.5 and 0.1 respectively. |