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..
Continual Multi-View Clustering with Consistent Anchor Guidance
Authors: Chao Zhang, Deng Xu, Xiuyi Jia, Chunlin Chen, Huaxiong Li
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness and efficiency of our method compared with some stateof-the-art approaches. |
| Researcher Affiliation | Academia | 1Department of Control Science and Intelligence Engineering, Nanjing University 2School of Computer Science and Engineering, Nanjing University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 ACMVC algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | Yes | We adopt seven popular multi-view datasets for experiments, including BBCSport1, Mfeat2, Wiki3, MITIndoor4, Caltech1015, Fashion6, and VGGFace7 datasets. Footnotes provide URLs such as http://mlg.ucd.ie/datasets/bbc.html and https://archive.ics.uci.edu/dataset/72/multiple+features for these datasets. |
| Dataset Splits | No | To construct the streaming data, we split the whole dataset into several chunks, and a chunk of data arrives in each round. The chunk size is set to 100 for BBCSport, 1000 for Mfeat, Wiki, MITIndoor, Caltech101, and 5000 for Fashion and VGGFace. This describes a streaming data setup, not a fixed training/validation/test split. |
| Hardware Specification | Yes | The computing platform is MATLAB R2019b with Win10 System, Intel Core i7-8700 CPU@3.2GHz and 16GB RAM. |
| Software Dependencies | Yes | The computing platform is MATLAB R2019b with Win10 System, Intel Core i7-8700 CPU@3.2GHz and 16GB RAM. |
| Experiment Setup | Yes | For our ACMVC, we search the parameters α and β in the range of {100, 101, ..., 106}, and fix the number of anchors k = 5c in all experiments for simplicity, where c is the number of clusters. |