Continual Multi-View Clustering with Consistent Anchor Guidance
Authors: Chao Zhang, Deng Xu, Xiuyi Jia, Chunlin Chen, Huaxiong Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |