Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
Authors: Weixuan Liang, En Zhu, Shengju Yu, Huiying Xu, Xinzhong Zhu, Xinwang Liu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments to verify the clustering performance of the proposed method and the correctness of the proposed theoretical results. |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology, Changsha, China 2School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China. |
| Pseudocode | Yes | Algorithm 1 Scalable Multiple Kernel Clustering |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code for their methodology publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | All the URLs of used datasets are listed in Section B of the appendix. 1. 100Leaves: https://archive.ics.uci.edu/dataset/241/one+hundred+plant+species+ leaves+data+set |
| Dataset Splits | No | The paper mentions using "training set" and "benchmark datasets" and also discusses "test" sets for evaluation, but it does not specify explicit training/validation/test split percentages, absolute sample counts for each split, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | All experiments are conducted on a desktop with Intel(R) Core(TM)-i7-10870H CPU. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We use the default setting for all the comparison algorithms according to the corresponding papers. ... For each view, the Gaussian kernel function is used to construct the kernel matrix, and the width δ2 is specified as the mean of the pairwise squared distances. In SMKC, we fix the number of anchors as 1000 for sufficient anchors... |