Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Auto-Weighted Multi-View Clustering
Authors: Pengzhen Ren, Yun Xiao, Pengfei Xu, Jun Guo, Xiaojiang Chen, Xin Wang, Dingyi Fang
IJCAI 2018 | Venue PDF | 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 EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| 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. |