Tensorial Multi-view Clustering with Deep Anchor Graph Projection
Authors: Wei Feng, Dongyuvan Wei, Qianqian Wang, Bo Dong
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple datasets demonstrate TMVC-DAGP s effectiveness and superiority. ... We evaluate the performance of the proposed method on eight widely adapted multi-view learning benchmark datasets... |
| Researcher Affiliation | Academia | 1College of Information Engineering, Northwest A&F University, Yangling, China 2School of Computer Science and Technology, Xi an Jiaotong University, Xi an, China 3School of Telecommunications Engineering, Xidian University, Xi an, China 4School of Continuing Education, Xi an Jiaotong University, Xi an, China |
| Pseudocode | No | The paper describes the optimization steps with mathematical equations (e.g., Eq. 5-21) and descriptive text, but does not present a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of the proposed method on eight widely adapted multi-view learning benchmark datasets, which are Yale [Yale University, 2001], BBCSport [Greene and Cunningham, 2006], Sonar [Sejnowski and Gorman, ], Vehicle Sensor [Duarte and Hu, 2004], NGs [Hussain et al., 2010],Web KB [Blum and Mitchell, 1998],MSRC [Winn and Jojic, 2005] and Sentences NYU v2 (RGB-D) [Silberman et al., 2012]. |
| Dataset Splits | No | The paper states: 'Data normalization was performed as a preprocessing step for all datasets to ensure consistent input quality.' but does not provide specific details on how datasets were split into training, validation, or test sets for reproduction. |
| Hardware Specification | Yes | All experiments were executed on a desktop with an Intel(R) Core(TM) i5-13400 CPU and 32 GB of RAM, using MATLAB 2023a. |
| Software Dependencies | Yes | All experiments were executed on a desktop with an Intel(R) Core(TM) i5-13400 CPU and 32 GB of RAM, using MATLAB 2023a. |
| Experiment Setup | Yes | For the deep anchor graph projection approach, the layer size configurations were determined based on the complexity of the dataset. Specifically, for a two-layer projection, sizes [d1, k] were used, where d1 varied among [4k, 5k, 6k]. For a three-layer configuration, sizes [d1, d2, k] were used, with d1 in the range of [8k, 10k, 12k] and d2 between [4k, 5k, 6k]. These configurations allowed us to explore the impact of depth in the deep projection on the clustering results. |