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.
Fast Incomplete Multi-view Clustering by Flexible Anchor Learning
Authors: Yalan Qin, Guorui Feng, Xinpeng Zhang
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 19,067 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,716 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 | Experiments conducted on different datasets confirm the superiority of FIML compared with other clustering methods for incomplete multi-view data. In this section, we perform experiments to validate the effectiveness and efficiency of FIML on several widely used multi-view datasets. |
| Researcher Affiliation | Academia | 1 School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China. Correspondence to: Guorui Feng <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Algorithm of FIML |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There is no explicit statement about releasing code, nor a link to a code repository. |
| Open Datasets | Yes | The experiments are conducted on several widely adopted datasets including news groups (NGs), Web KB, ORL, STL10, MNIST and Cifar100. |
| Dataset Splits | No | The paper mentions using well-known datasets and evaluates clustering performance under different 'missing ratios' but does not specify explicit training, validation, or testing splits for model development or evaluation, which is common in clustering tasks where the entire dataset is typically clustered. |
| Hardware Specification | Yes | We run all experiments on AMD Ryzen 5 Six-Core Processor 3.60 GHz. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used to implement the proposed method. |
| Experiment Setup | Yes | There are total two parameters appeared in FIML, including the trade-off parameter λ and the number of anchors m. We then perform experiments on different datasets to study how these two parameters influence the final clustering performance. We set λ and m in the range of [0.001, 0.1, 1, 10, 100, 1000] and [k, 2k, 3k, 5k, 7k], respectively. Here, k corresponds to the total number of clusters in dataset. |