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..
Learning Cluster-Wise Anchors for Multi-View Clustering
Authors: Chao Zhang, Xiuyi Jia, Zechao Li, Chunlin Chen, Huaxiong Li
AAAI 2024 | Venue PDF | 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. |