Unified Spectral Clustering With Optimal Graph
Authors: Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches. Table 1: Description of the data sets. Table 2: Clustering results obtained on benchmark data sets. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, China 2Department of Computer Science, Southern Illinois University, Carbondale, USA 3Institute of Biomedical Informatics and Department of Computer Science, University of Kentucky, Lexington, USA |
| Pseudocode | Yes | Algorithm 1 The algorithm of SCSK. Algorithm 2 The algorithm of SCMK. |
| Open Source Code | Yes | For the purpose of reproducibility, the code is publicly available11. 11https://github.com/sckangz/AAAI18 |
| Open Datasets | Yes | There are altogether ten real benchmark data sets used in our experiments. Table 1 summarizes the statistics of these data sets. ... http://www-users.cs.umn.edu/ han/data/tmdata.tar.gz http://www.cad.zju.edu.cn/home/dengcai/Data/Text Data.html ... YALE, ORL, AR, and JAFEE contain images of individuals. ... http://vision.ucsd.edu/content/yale-face-database http://www2.ece.ohio-state.edu/ aleix/ARdatabase.html http://www.kasrl.org/jaffe.html http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php http://www.cs.nyu.edu/ roweis/data.html |
| Dataset Splits | No | The paper uses benchmark datasets and mentions general experimental settings (e.g., 'We set the number of clusters to the true number of classes'), but does not provide specific training, validation, or test split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using and comparing with various algorithms and their implementations (e.g., KKM, SC, RKKM, MKKM, AASC, RMKKM) and provides links to some of their codebases, but does not specify software dependencies with version numbers for reproducibility of their own code (e.g., Python 3.x, specific library versions). |
| Experiment Setup | Yes | There are three parameters in our model: α, β, and γ. We use YALE data set as an example to demonstrate the sensitivity of our model SCMK to parameters. As shown in Figure 1, our model is quite insensitive to α and β, and γ over wide ranges of values. |