Learning Cluster-Wise Anchors for Multi-View Clustering
Authors: Chao Zhang, Xiuyi Jia, Zechao Li, Chunlin Chen, Huaxiong Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results demonstrate the effectiveness of our proposed method compared with some state-of-the-art MVC 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: CAMVC algorithm Input: Multi-view data {Xi}v i=1, cluster number k, parameters α, β, m. 1: Initialize Ai = 0, Hi = 0. 2: Construct the prior cluster indicator matrix Y. 3: while not converged do 4: Update Z by Eq. (7); 5: Update {Ai}v i=1 by Eq. (9); 6: Update {Hi}v i=1 by solving (11); 7: end while Output: Perform k-means on Z to obtain the clusters. |
| Open Source Code | No | For a fair comparison, we use the official codes of baselines. The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We conduct experiments on seven popular datasets, including MSRC (Chen et al. 2021), BBCSport1, Wiki2, Notting Hill (Chen et al. 2021), Caltech1013, Fashion (Xiao, Rasul, and Vollgraf 2017), and MNIST4. Table 2 shows the general statistics of these datasets. (Footnotes provide URLs: 1http://mlg.ucd.ie/datasets/bbc.html 2http://www.svcl.ucsd.edu/projects/crossmodal/ 3https://paperswithcode.com/dataset/caltech-101 4http://yann.lecun.com/exdb/mnist/) |
| Dataset Splits | No | For our CAMVC, we tune α in the range of {10 3, 10 2, ..., 101}, β in {10 1, 10 2, ..., 103}, and m in {1, 3, 5}. ... The paper does not provide specific train/validation/test dataset splits, only mentions parameter tuning. |
| Hardware Specification | Yes | All experiments are conducted using MATLAB 2017b with i5-1230 CPU and 16GB RAM. |
| Software Dependencies | Yes | All experiments are conducted using MATLAB 2017b with i5-1230 CPU and 16GB RAM. |
| Experiment Setup | Yes | For our CAMVC, we tune α in the range of {10 3, 10 2, ..., 101}, β in {10 1, 10 2, ..., 103}, and m in {1, 3, 5}. ... following (Wang et al. 2022; Wan et al. 2023), we run 50 times k-means on final representation to report the best results. The optimal parameters of baselines are tuned by grid search in suggested ranges. |