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 [1].
Cross-View Diversity Embedded Consensus Learning for Multi-View Clustering
Authors: Chong Peng, Kai Zhang, Yongyong Chen, Chenglizhao Chen, Qiang Cheng
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results confirm the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Qingdao University, China 2School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China 3College of Computer Science and Technology, China University of Petroleum (East China), China 4Department of Computer Science, University of Kentucky, USA |
| Pseudocode | No | The paper describes optimization steps in detail but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In particular, we use six benchmark data sets, including the BBC-4view, BBC-Sport, Flowers, UCI-3view, Still DB, and MITindoor, and four evaluation metrics, including the clustering accuracy (ACC), normalized mutual information (NMI), adjusted rand index (AR), and F-Score, of which the detailed descriptions can be found in [Wu et al., 2019; Larson, 2019], respectively. |
| Dataset Splits | No | For all methods, the final clustering step is repeated 10 times and we report the averaged results with parameters tuned to the best. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all balancing parameters, we tune them within the set {0.001,0.01,0.1,1,10,100,1000}. For Ï, Îș, and N, we fix them to 0.001, 1.5, and 5 throughout the paper. If not otherwise clarified, we use a third-order CCLMVC in the experiment. |