Multi-View Multiple Clusterings Using Deep Matrix Factorization
Authors: Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang6348-6355
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions. and Experimental Results and Analysis In this section, we evaluate the effectiveness and efficiency of our proposed DMClusts on seven widely-used multi-view datasets, as described in Table 1. |
| Researcher Affiliation | Academia | Shaowei Wei,1 Jun Wang,1, Guoxian Yu,1,2 Carlotta Domeniconi,3 Xiangliang Zhang2 1College of Computer and Information Sciences, Southwest University, Chongqing, China 2CEMSE, King Abdullah University of Science and Technology, Thuwal, SA 3Department of Computer Science, George Mason University, VA, USA |
| Pseudocode | No | The paper describes an 'iterative optimization procedure' and provides update rules, but does not present a formal pseudocode block or algorithm. |
| Open Source Code | Yes | The code and Supplementary file of DMClusts is available at http://mlda.swu.edu.cn/codes.php?name=DMClusts. |
| Open Datasets | Yes | In this section, we evaluate the effectiveness and efficiency of our proposed DMClusts on seven widely-used multi-view datasets, as described in Table 1. The adopted datasets are from different domains, with different numbers of views and objects. More details on the data are given in the Supplementary file. |
| Dataset Splits | No | The paper mentions 'average results of ten independent runs' but does not specify explicit training, validation, and test splits with percentages or sample counts for the datasets. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU models, CPU types, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, or specific library versions) used for the experiments. |
| Experiment Setup | Yes | The input parameters of DMClusts are selected from the following ranges: r {5 10 4, 5 10 3, ..., 5}, λ {10 4, 10 3, ..., 104}, β [0, 1] and K1 [k, min(dv)], K2 [k, K1] with M = 2. We fix the number of clusters for each clustering to the number of classes c of each dataset, as reported in Table 1. and we constructed a synthetic dataset on Reuters by injecting a noisy view X(6) R500 1200 following standard Gaussian distribution. We then apply DMF and DMClusts on this synthetic dataset with the input parameters fixed as r = 0.1, λ = 0.1, β = 0.4. |