Multi-View Clustering on Topological Manifold
Authors: Shudong Huang, Ivor Tsang, Zenglin Xu, Jiancheng Lv, Quan-Hui Liu6944-6951
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Substantial experiments on benchmark datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance. |
| Researcher Affiliation | Academia | 1 College of Computer Science, Sichuan University, Chengdu 610065, China 2 Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Sydney, NSW 2007, Australia 3 School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China |
| Pseudocode | Yes | Algorithm 1: Algorithm to solve Eq. (13) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Several benchmark multi-view data sets are used in this paper: 3source, Handwritten numerals, Caltech7 and Caltech20. |
| Dataset Splits | No | The paper does not provide specific training/validation dataset splits, as is common for unsupervised clustering tasks where the entire dataset is often used for the clustering process. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Input: Initial graphs {G(1), G(2), . . . , G(m)} for the m views, cluster number c, parameters α and β. Initialize the weight of each view µ(v) = 1/m. Initialize the consensus graph S = Pm v=1 µ(v)G(v). For simplicity, we search both α and β in the range [0.05, 0.1, 0.5, 1, 2, 5, 10]. Motivated by (Nie, Cai, and Li 2017), we initialize the initial graphs G(v) by selecting 20-nearest neighbors among raw data. |