Priori Anchor Labels Supervised Scalable Multi-View Bipartite Graph Clustering

Authors: Jiali You, Zhenwen Ren, Xiaojian You, Haoran Li, Yuancheng Yao

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimentally, abundant of experiments are accomplished on six scalable benchmark datasets, and the experimental results fully demonstrate the effectiveness and efficiency of our SMGC.
Researcher Affiliation Academia 1School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang, China 2Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China 3Song Shan Laboratory, Henan, China 4Department of School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, China
Pseudocode Yes Algorithm 1 The algorithm of SMGC
Open Source Code No The paper does not contain any explicit statement about providing open-source code or a link to a code repository.
Open Datasets Yes We extensively evaluate the clustering performance of our proposed SMGC on six real multiview benchmark datasets, including Caltech101-20 (Wu et al. 2022), CCV (Wang et al. 2021), Caltech101-all (Sun et al. 2021), SUNRGBD (Liu et al. 2021), NUSWIDEOBJ (Du et al. 2021), and Aw A (Yang et al. 2022a).
Dataset Splits No The paper mentions using benchmark datasets but does not provide specific details on training, validation, and test splits (e.g., percentages, sample counts, or explicit splitting methodology).
Hardware Specification No The paper does not specify any hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments.
Software Dependencies No The paper does not provide specific software dependency details, such as library names with version numbers, used to replicate the experiment.
Experiment Setup Yes In this section, we utilize the grid search to adjust parameter β with the range of [10 5, 10 4, , 101, 102], so as to study the influence of parameter in SMGC.