Metric Multi-View Graph Clustering
Authors: Yuze Tan, Yixi Liu, Hongjie Wu, Jiancheng Lv, Shudong Huang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical studies corroborate our theoretical findings and demonstrate that the proposed method is able to boost the multi-view clustering performance. |
| Researcher Affiliation | Academia | College of Computer Science, Sichuan University, Chengdu 610065, China {tanyuze, liuyixi}@stu.scu.edu.cn, wuhongjie0818@gmail.com {lvjiancheng, huangsd}@scu.edu.cn |
| Pseudocode | Yes | Algorithm 1: Algorithm for Metric Multi-view Subspace Clustering |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | HAR is a Human Activity Recognition dataset which consists of 2941 samples and 6 classes. Cora is a popular dataset which is composed of 2708 instances and 7 categories, MSRC includes 240 images and 8 classes. ... Newsgroups (NGs) ... ORL is a well-known human face dataset ... Yale contains 165 samples ... |
| Dataset Splits | No | The paper lists several datasets (HAR, Cora, MSRC, Newsgroups, ORL, Yale) but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU or CPU models, memory, or cloud computing resources) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | With the aim of studying what impact different parameter settings will have on the clustering results, we vary three parameters: γ and ϕ in the ranges[1, 1e1, 1e2, 1e3, 1e4, 1e5], and the filter order k in [1, 2, 3, 4, 5, 6] respectively. |