One-Pass Incomplete Multi-View Clustering
Authors: Menglei Hu, Songcan Chen3838-3845
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments conducted on four real datasets demonstrate the efficiency and effectiveness of the proposed OPIMC method. |
| Researcher Affiliation | Academia | 1College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics 2Collaborative Innovation Center of Novel Software Technology and Industrialization |
| Pseudocode | Yes | Algorithm 1 One-Pass Incomplete Multi-view Clustering |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | In this paper, we conduct the experiments on four real-world multi-view datasets, which contains two small datasets and two large datasets... The important statistics of these datasets are given in the Table 1. (Footnotes providing URLs: 1http://vikas.sindhwani.org/manifoldregularization.html 2http://archive.ics.uci.edu/ml/datasets/Multiple+Features 3http://archive.ics.uci.edu/ml/machine-learning-databases/00259/ 4https://archive.ics.uci.edu/ml/datasets/You Tube+Multiview+Video+Games+Dataset) |
| Dataset Splits | No | The paper mentions 'incomplete rate' and 'chunk size' but does not explicitly state specific training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All the experiments are run on computer with Intel(R)390 Core(TM) i5-3470 @ 3.20GHz CPU and 16.0 GB RAM with the help of Matlab R2013a. |
| Software Dependencies | Yes | All the experiments are run on computer with Intel(R)390 Core(TM) i5-3470 @ 3.20GHz CPU and 16.0 GB RAM with the help of Matlab R2013a. |
| Experiment Setup | Yes | We search the parameter α in {1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3}. The chunk size s for online methods is set to 50 for small datasets and 2000 for large datasets, respectively. It is worth noting that through experiments we find that OPIMC converges quickly, thus setting L = 20 is enough. |