Unified K-Means Clustering with Label-Guided Manifold Learning
Authors: Qianqian Wang, Mengping Jiang, Zhengming Ding, Quanxue Gao
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The detailed experimental results demonstrate the efficacy of our proposed methodology. In this chapter, we conducted relevant experiments on two toy datasets and ten benchmark datasets, and selected 6 classic clustering comparison algorithms for comparison. |
| Researcher Affiliation | Academia | 1School of Communication Engineering, Xidian University, Xi an, China. 2Department of Computer Science, Tulane University, New Orleans, LA. Correspondence to: Quanxue Gao <EMAIL>. |
| Pseudocode | Yes | The comprehensive flow of the algorithm can be found in Algorithms 1 and 2. Algorithm 1 Optimizing F Algorithm 2 Solving problem (24) |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Datasets: We selected the following ten datasets: CMUPIE (Sim et al., 2002), digits (Kusetogullari et al., 2020), FERET (Phillips et al., 2000), Mpeg7 (Bober, 2001), olivetti (Samaria & Harter, 1994), Palm1, Pengdigits (Liu & Wechsler, 1997), PEAL (Wang & Tang, 2004), STL10 (Coates et al., 2011), and USPS (Hull, 1994). Detailed information on the datasets is provided in the Appendix A. |
| Dataset Splits | No | The paper mentions dividing toy datasets into clusters (e.g., "There are a total of four hundred samples, divided into two clusters, with 200 samples in each cluster" for the Two-spiral Dataset). However, it does not provide specific training, validation, or test splits for any of the datasets used in the experiments. |
| Hardware Specification | Yes | Our experiments were conducted on a Windows 11 system, 13th Gen Intel(R) Core(TM) CPU, and MATLAB R2023a. |
| Software Dependencies | Yes | Our experiments were conducted on a Windows 11 system, 13th Gen Intel(R) Core(TM) CPU, and MATLAB R2023a. |
| Experiment Setup | Yes | A.4. Parameter setting Since Equation (24) contains the parameter λ, we need to set the parameter λ for all four distances. In addition, Our-KNN needs to set the value of the nearest neighbor K; using the Gaussian kernel function K(x, y) = exp(−kx − yk2 /σ2 ), Our-K-ED contains the parameter σ. ... Our-ED: The values of λ for CMUPIE, digits, FERET, Mpeg7, olivetti, Palm, Pengdigits, PEAL, STL10, and USPS are 7696000, 2886000, 1000, 1200, 1801000, 13126000, 500000, 88000, 500000, 52600. |