Robust Contrastive Multi-view Kernel Clustering
Authors: Peng Su, Yixi Liu, Shujian Li, Shudong Huang, Jiancheng Lv
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted comprehensive experiments on various MKC methods to validate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1College of Computer Science, Sichuan University, Chengdu 610065, China 2Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, Chengdu 610065, China |
| Pseudocode | No | The paper describes the method using mathematical equations and textual explanations, but does not include a formally structured pseudocode block or an 'Algorithm' label. |
| Open Source Code | Yes | The code is available at https://github.com/Duolaimi/rcmk main. |
| Open Datasets | Yes | Cora [Bisson and Grimal, 2012] is composed of 4 views, including content, inbound, outbound, and cites, of the documents, containing 2708 samples categorized into 7 labels. Handwritten [Nie et al., 2017] is a digit dataset comprising a total of two thousand samples distributed across 10 classes, with each class containing 200 samples. |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits, such as percentages, absolute counts, or explicit splitting methodologies. |
| Hardware Specification | No | The paper mentions 'GPU acceleration can be utilized to shorten the training time' but does not provide specific hardware details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using the 'Adam algorithm' but does not provide specific version numbers for Adam or any other software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | We explore the parameter t in the range of [0.8, 0.9, 1.0, 1.1, 1.2] and dl in the range of [8, 16, 32, 64, 128, 256]. For the sake of presentation, we do not exhaustively list all available parameters. Many datasets exhibit optimal or satisfactory performance within this parameter range. Taking the aforementioned four datasets as examples, the clustering results are illustrated in Figure 3. It can be observed that the dimensionality dl of the projection feature is a more critical parameter. |