Doubly Aligned Incomplete Multi-view Clustering
Authors: Menglei Hu, Songcan Chen
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four real-world datasets demonstrate its advantages. Section 4 Experiments and Analysis |
| Researcher Affiliation | Academia | Menglei Hu and Songcan Chen College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization {ml.hu, s.chen}@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 Optimization of DAIMC |
| Open Source Code | No | The paper does not provide any specific repository link or explicit statement about the release of the source code for the methodology described. |
| Open Datasets | Yes | Datasets: The experiments are conducted on four real-world multi-view datasets. The important statistics of these datasets are given in the Table 1. 1http://www.svcl.ucsd.edu/projects/crossmodal/ 2http://archive.ics.uci.edu/ml/datasets.html 3http://mlg.ucd.ie/datasets/3sources.html |
| Dataset Splits | No | The paper mentions randomly removing instances to simulate incompleteness ('for the complete datasets, we randomly remove some instances from each view to make the views incomplete. The incomplete rate is from 0 (all the views are complete) to 0.5 (all the views have 50% instances missing).'), but it does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or splitting methodology). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'lyap function of MATLAB' but does not specify a version number for MATLAB or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The proposed DAIMC method contains two hyperparameters {α, β}. We conduct the hyper-parameter experiments on Digit dataset. We set the incomplete rate as 0.3 and 0.5 respectively, and report the clustering performance of DAIMC by ranging α and β within the set of {1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3}. and for multi-view clustering, the K is set to the number of the categories of the data matrix X(v), i.e., K = C. |