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..
Clustering Ensemble Meets Low-rank Tensor Approximation
Authors: Yuheng Jia, Hui Liu, Junhui Hou, Qingfu Zhang7970-7978
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 11 state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR |
| Pseudocode | Yes | Algorithm 1 t-SVD of a 3-D tensor (Zhang et al. 2014) and Algorithm 2 Numerical solution to Eq. (9) are provided. |
| Open Source Code | Yes | To reproduce the results, we made the code publicly available at https://github.com/jyhlearning/Tensor Clustering Ensemble. |
| Open Datasets | Yes | Following recent clustering ensemble papers (Huang, Wang, and Lai 2018; Huang, Lai, and Wang 2016; Zhou, Zheng, and Pan 2019), we adopted 7 commonly used data sets, i.e., Bin Alpha, Multiple features (MF), MNIST, Semeion, Cal Tech, Texture and ISOLET. |
| Dataset Splits | No | The paper does not explicitly provide details about train/validation/test dataset splits. It mentions randomly selecting samples and base clusterings for repetitions, but not data partitioning for validation purposes. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | For the compared methods, we set the hyper-parameters according to their original papers. If there are no suggested values, we exhaustively searched the hyper-parameters, and used the ones producing the best performance. The proposed model only contains one hyperparameter λ, which was set to 0.002 for all the data sets. |