Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Consensus-Guided Incomplete Multi-view Clustering via Cross-view Affinities Learning
Authors: Qian Liu, Huibing Wang, Jinjia Peng, Yawei Chen, Mingze Yao, Xianping Fu, Yang Wang
IJCAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 20,977 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,596 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on seven benchmark datasets demonstrate that CAL outperforms some state-of-the-art methods in clustering performance. Table 1: Clustering results (%) w.r.t. three metrics on BBCSport, MSRC, ORL and BBC datasets with different missing rates. |
| Researcher Affiliation | Academia | 1School of Information Science and Technology, Dalian Martime University, Dalian, China 2School of Cyber Security and Computer, Hebei University, Baoding, China 3School of Computer Science and Information Engineering, Hefei University of Technology, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 CAL Algorithm |
| Open Source Code | Yes | The code is available at https://github.com/whbdmu/CAL. |
| Open Datasets | Yes | We evaluate our method on seven popular multi-view datasets, including BBCSport, MSRC, ORL, BBC, Caltech101-7, Flower and Handwritten datasets. Table 2 lists the general statistics. |
| Dataset Splits | Yes | For each dataset, we arbitrarily remove 10%, 30%, 50%, and 70% samples from each view to construct incomplete multi-view data, while ensuring that each sample exists in at least one view. |
| Hardware Specification | No | No specific hardware details are provided in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided in the paper. |
| Experiment Setup | Yes | For our CAL, we select λ1 and λ2 within the range of [10 5, 103] and evaluate our method using three popular metrics: accuracy (ACC), normalized mutual information (NMI), and purity. Generally, CAL obtains ideal clustering results when p is selected from [0.8, 0.9]. |