Multi-View Multiple Clustering
Authors: Shixin Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows that MVMC (MVMCC) can exploit multi-view data to generate multiple high-quality and diverse clusterings (co-clusterings), with superior performance to the state-of-the-art methods. 3 Experimental Results and Analysis In this section, we evaluate our proposed MVMC and MVMCC on five widely-used multi-view datasets [Li et al., 2015; Tao et al., 2018], which are described in Table 1. |
| Researcher Affiliation | Academia | 1College of Computer and Information Sciences, Southwest University, Chongqing, China 2Department of Computer Science, George Mason University, VA, USA 3CEMSE, King Abdullah University of Science and Technology, Thuwal, SA {ysx051836, gxyu, kingjun}@swu.edu.cn, carlotta@cs.gmu.edu, xiangliang.zhang@kaust.ac.sa |
| Pseudocode | No | The paper refers to supplementary material for optimization details: 'The detailed optimization process can be viewed in the supplementary file due to the limitation of space.' However, no pseudocode or algorithm block is present in the main text. |
| Open Source Code | Yes | The code of MVMC (MVMCC) is available at http://mlda.swu.edu.cn/codes.php?name=MVMC. |
| Open Datasets | Yes | In this section, we evaluate our proposed MVMC and MVMCC on five widely-used multi-view datasets [Li et al., 2015; Tao et al., 2018], which are described in Table 1. The datasets have different number of views and are from different domains. Caltech-71 and Caltech-20 [Li et al., 2015] are two subsets of Caltech-101... Mul-fea digits2 is comprised of 2,000 data points... Wiki article3 contains selected sections... Corel4 [Tao et al., 2018] consists of 5000 images... Mirflickr5 contains 25,000 instances... |
| Dataset Splits | No | The paper does not explicitly provide details about specific training/validation/test splits, percentages, or the methodology for partitioning the datasets. It mentions 'average results (of ten independent runs)' but this pertains to repeated experiments rather than data splitting for training and validation. |
| Hardware Specification | Yes | The experiments are conducted on a server with Ubuntu 16.04, Intel Xeon8163 with 1TB RAM; all methods are implemented in Matlab2014a. |
| Software Dependencies | Yes | The experiments are conducted on a server with Ubuntu 16.04, Intel Xeon8163 with 1TB RAM; all methods are implemented in Matlab2014a. |
| Experiment Setup | Yes | The parameter values of MVMC and MVMCC are λ1 = 10, λ2 = 100, and h = 2 for multiple one-way clusterings, and h = m for multiple co-clusterings. We fix the number of row-clusters rk for each clustering as the respective number of classes of each dataset, as listed in Table 1. For coclustering, we adopt a widely used technique [Monti et al., 2003] to determine the number of column clusters ck. Detailed parameter values are given in the supplementary file. We investigate the sensitivity of MVMC to these parameters by varying λ1 (it controls diversity) and λ2 (it controls quality) in the range [10−3, 10−2, ..., 103]. |