Hierarchical Multiple Kernel Clustering
Authors: Jiyuan Liu, Xinwang Liu, Siwei Wang, Sihang Zhou, Yuexiang Yang8671-8679
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on benchmark datasets. It can observed that the proposed algorithm outperforms other representative MKC algorithms. |
| Researcher Affiliation | Academia | Jiyuan Liu, 1 Xinwang Liu, 1* Siwei Wang, 1 Sihang Zhou, 2 Yuexiang Yang 1 1 College of Computer, National University of Defense Technology, Changsha, Hunan, China 2 College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China {liujiyuan13, xinwangliu, wangsiwei13}@nudt.edu.cn, sihangjoe@gmail.com, yyx@nudt.edu.cn |
| Pseudocode | Yes | Algorithm 1: Hierarchical multiple kernel clustering |
| Open Source Code | Yes | Meanwhile, we open the source code of HMKC on Github9. 9https://github.com/liujiyuan13/HMKC-code release |
| Open Datasets | Yes | We evaluate the proposed algorithm on eight widely used MKC benchmark datasets, including AR10P1, BBCSport2, CCV3, Flower174, Flower1025, Ionosphere6, Heart7 and Plant8. Their details are shown in Table 1. |
| Dataset Splits | No | The paper does not explicitly describe a validation dataset split (e.g., 'validation set' or 'validation split') or cross-validation methodology for model training or selection. The term 'validation' is used in the context of 'Validation on Intermediary Matrix' to discuss the approach itself, not a data split for model evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions) required to replicate the experiments. |
| Experiment Setup | Yes | Only the sizes of the intermediary matrices, {H(t) p }s t=1 are supposed to be given manually. In the following, we consider two experimental settings. The first is called HMKC-1 with employing 1-layer of intermediary matrices, {H(1) p }m p=1 Rn c, where c is searched from [2k, 3k, , 20k]. While the second one is named HMKC-2 with employing 2-layer of intermediary matrices, {H(1) p }m p=1 Rn c1 and {H(2) p }m p=1 Rn c2, in which c1 c2 and c1, c2 are grid searched from [2k, 3k, , 20k]. In specific, the search region is restricted to [2k, 3k, , 10k] for AR10P since it has relatively small number of samples in each cluster. |