Self-supervised Weighted Information Bottleneck for Multi-view Clustering
Authors: Zhengzheng Lou, Chaoyang Zhang, Hang Xue, Yangdong Ye, Qinglei Zhou, Shizhe Hu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multi-view text, multi-feature image, multi-angle video, and multi-modal text-image dataset as well as large-scale datasets show the superiority of the SWIB method. |
| Researcher Affiliation | Academia | School of Computer and Artificial Intelligence, Zhengzhou University, China |
| Pseudocode | Yes | Algorithm 1 Algorithm for Optimizing the SWIB |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it explicitly state that code is released. |
| Open Datasets | Yes | 20NGs dataset 1 has 500 documents from the popular 20 Newsgroups dataset. Every document is processed by three various methods, corresponding to each view of the dataset. 1http://lig-membres.imag.fr/grimal/data.html" "COIL20 dataset 2 contains images of 20 different objects on a motorized turntable. 2https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php" "WVU dataset 3 contains human action videos of 10 types captured from different angles. 3https://community.wvu.edu/vkkulathumani/wvu-action.html" "PASCAL dataset 4 has 20 clusters with 1000 data samples, and they are captured from image and text aspect, where the images are extracted with SIFT representation and the documents are extracted with Bo W model. 4https://aclanthology.org/W10-0721.pdf |
| Dataset Splits | No | For our SWIB method, we set β as + and search the parameter λ from the range {0.1, 0.2, 0.4, 0.6, 0.8}. Figure 4 shows the clustering Acc and NMI results corresponding to each parameter setting. From this figure, we find that the clustering results maintain almost stable on the parameter search range, and suggests that it is not difficult to select a better parameter in usage and also exhibits possible practicability of SWIB method into real-world scenario. Note that we set λ = 0.2 for 20NGs, WVU and PASCAL datasets, and λ = 0.4 for COIL20 dataset. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or processor types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For our SWIB method, we set β as + and search the parameter λ from the range {0.1, 0.2, 0.4, 0.6, 0.8}. ... Note that we set λ = 0.2 for 20NGs, WVU and PASCAL datasets, and λ = 0.4 for COIL20 dataset. |