Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Multiple Kernel Clustering
Authors: Jiyuan Liu, Xinwang Liu, Siwei Wang, Sihang Zhou, Yuexiang Yang8671-8679
AAAI 2021 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |