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 [1].
LRGR: Self-Supervised Incomplete Multi-View Clustering via Local Refinement and Global Realignment
Authors: Yanwanyu Xi, Xiao Zheng, Chang Tang, Xingchen Hu, Yuanyuan Liu, Jun-Jie Huang, Xinwang Liu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five widely used multi-view datasets demonstrate the competitiveness and superiority of our method compared to state-of-the-art approaches. Extensive experimental results verify the effectiveness of LRGR and show considerable improvement over ten state-of-the-art methods on five multi-view datasets. Section 4 is titled "Experiment" and details experimental settings, comparisons, and ablation studies. |
| Researcher Affiliation | Academia | 1School of Computer Science, China University of Geosciences, Wuhan, China 2School of Computer Science, Hubei University of Technology, China 3School of Software Engineering, Huazhong University of Science and Technology, China 4College of Systems Engineering, National University of Defense Technology, China 5College of Computer Science and Technology, National University of Defense Technology, China. All listed institutions are universities, and email domains are consistent with academic affiliations. |
| Pseudocode | No | The paper describes the methodology in narrative text and uses flowcharts (Figure 2, Figure 3) to illustrate components, but it does not contain any explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We evaluate our proposed model on five widely used datasets. Specifically, CUB [Wah et al., 2011] with 600 samples, 2 views. DHA [Lin et al., 2012] with 383 samples, 2 views. COIL20 [Wan et al., 2021] with 1440 samples, 3 views. YTF10 [Wolf et al., 2011] with 38654 samples, 4 views, and UCI [Asuncion et al., 2007] with 2000 samples, 3 views. |
| Dataset Splits | No | The paper mentions generating missing data at various rates (e.g., "missing rate η is defined as η = m/n") and evaluates clustering performance on the datasets. However, it does not provide specific training/test/validation splits for the data itself, which would be needed for reproducing a traditional supervised learning experiment. |
| Hardware Specification | Yes | We implement our method using Py Torch 1.12 and conduct experiments on a standard Ubuntu 20.04.6 OS with an NVIDIA GTX 1060 GPU. |
| Software Dependencies | Yes | We implement our method using Py Torch 1.12 and conduct experiments on a standard Ubuntu 20.04.6 OS with an NVIDIA GTX 1060 GPU. |
| Experiment Setup | Yes | Adam [Kingma, 2014] is chosen as the optimizer with the 0.0005 and 0.0001 learning rate in the pre and align training process. In pretrain stage, we update the network only with reconstruction loss in Eq.(4). And in the second stage, we update the network with Eq.(12). For comparison methods, we use the recommended parameters and network structures to ensure the best performance. In this paper, n is set as 32. We set the α and β varies from [0.0001, 0.001, 0.01, 0.1, 1]. |