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..
Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding
Authors: Guoqing Chao, Yi Jiang, Dianhui Chu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments compared with state-of-the-art approaches demonstrated the effectiveness and superiority of our method. |
| Researcher Affiliation | Academia | Guoqing Chao, Yi Jiang, Dianhui Chu Harbin Institute of Technology, 2 West Culture Road, Weihai, Shandong 264209, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization of the proposed ICMVC |
| Open Source Code | Yes | Our code is publicly available at https://github.com/liunian-Jay/ICMVC. |
| Open Datasets | Yes | We used four commonly-used datasets in our experiments to evaluate our model. Scene-15: It consists of 4,485 images distributed in 15 scene categories with GIST and LBP features as two views. Land Use-21: It consists of 2100 satellite images from 21 categories with two views: PHOG and LBP. MSRC-V1: It is an image dataset consisting of 210 images in seven categories, including trees, buildings, airplanes, cows, faces, cars, and bicycles, with GIST and HOG features as two views. Noisy MNIST: the original images are used as view 1, and the sampled intra-class images with Gaussian white noise are used as view 2, and we use its subset containing 10k samples in the experiments. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits. It mentions varying missing rates for evaluation but not how the datasets themselves were partitioned for training or validation purposes. |
| Hardware Specification | Yes | We implement ICMVC in Py Torch 1.12.1 and conduct all the experiments on Ubuntu 20.04 with NVIDIA 2080Ti GPU. |
| Software Dependencies | Yes | We implement ICMVC in Py Torch 1.12.1 and conduct all the experiments on Ubuntu 20.04 with NVIDIA 2080Ti GPU. |
| Experiment Setup | Yes | The Adam optimizer is adopted, and the learning rate is set to 0.001, the hyper-parameter K is set to 10. The instance-level temperature parameter τI is fixed at 1.0, and the cluster-level parameter τC is fixed at 0.5. We observe that it can fully converges after 500 epoches after training the network, thus 500 epoches is set to terminate. |