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..
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
Authors: Yiding Lu, Yijie Lin, Mouxing Yang, Dezhong Peng, Peng Hu, Xi Peng
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art Mv C methods in both complete and incomplete Mv C settings. |
| Researcher Affiliation | Academia | College of Computer Science, Sichuan University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is released on https://github.com/XLearning SCU/2024-AAAI-DIVIDE. |
| Open Datasets | Yes | Scene-15 (Li and Perona 2005) contains 4,485 images of 15 scene categories. Caltech-101 (Li et al. 2015) consists of 8,677 images of objects from 101 classes. Reuters (Amini, Usunier, and Goutte 2009) is a multilingual news dataset with 18,758 samples from various languages. Land Use-21 (Yang and Newsam 2010) contains 2,100 satellite images from 21 classes. |
| Dataset Splits | Yes | Specifically, we randomly select m = η n samples and remove one view from each to simulate incomplete data, where η is the missing rate and n is the total number of samples. To warm up, we set the target T in Eq. (1) as an identity matrix In for the first 100 epochs and then adopt the rectified target Eq. (6) in the remaining epochs. |
| Hardware Specification | Yes | We implement our method in Py Torch 1.13.0 and run all experiments on NVIDIA 3090 GPUs in Ubuntu 20.04 OS. |
| Software Dependencies | Yes | We implement our method in Py Torch 1.13.0 and run all experiments on NVIDIA 3090 GPUs in Ubuntu 20.04 OS. |
| Experiment Setup | Yes | We train our model for 200 epochs using the Adam optimizer without weight decay. The initial learning rate is set to 2 10 3, and the batch size is fixed to 1024. To warm up, we set the target T in Eq. (1) as an identity matrix In for the first 100 epochs and then adopt the rectified target Eq. (6) in the remaining epochs. For the other hyper-parameters, we fix the contrastive temperature τ = 0.5, the temperature of the kernel function σ = 0.1, the walking step t = 5, and the rectified weight α = 0.5 throughout experiments. |