Efficient and Effective Incomplete Multi-View Clustering
Authors: Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Chang Tang, En Zhu, Jianping Yin, Wen Gao4392-4399
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, National University of Defense Technology, Changsha, China, 410073 2College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhengjiang, China, 321004 3Research Institute of Ningbo Cixing Co., Ltd, Ningbo, China, 315336 4School of Computer Science, China University of Geosciences, Wuhan, China, 430074 5Dongguan University of Technology, Guangdong, China 6School of Electronics Engineering and Computer Science, Peking University, Beijing, China, 100871 |
| Pseudocode | Yes | Algorithm 1 The Proposed EE-IMVC |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The proposed algorithm is experimentally evaluated on four widely used multiple kernel benchmarkdata sets shown in Table 2. They are Oxford Flower17 and Flower1021, Caltech1022 and Columbia Consumer Video (CCV)3. For these datasets, all kernel matrices are pre-computed and can be publicly downloaded from the above websites. 1http://www.robots.ox.ox.ac.uk/ vgg/data/flowers/ 2http://files.is.tue.mpg.de/pgehler/projects/iccv09/ 3http://www.ee.columbia.edu/ln/dvmm/CCV/ |
| Dataset Splits | No | The paper mentions generating incomplete patterns and repeating experiments for 50 times with random initialization, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers. |
| Experiment Setup | Yes | The parameter ε, termed missing ratio in this experiment, controls the percentage of samples that have absent views, and it affects the performance of the algorithms in comparison. To show this point in depth, we compare these algorithms with respect to ε. Specifically, ε on all the datasets is set as [0.1 : 0.1 : 0.9]. ... For all algorithms, we repeat each experiment for 50 times with random initialization to reduce the effect of randomness caused by k-means, and report the best result. Meanwhile, we randomly generate the incomplete patterns for 30 times in the above-mentioned way and report the statistical results. ... In our current imple-mentation, we simply initialize {H(u) p }m p=1 as zeros, and {Wp}m p=1 as identity matrix. ... For all data sets, it is assumed that the true number of clusters k is known and it is set as the true number of classes. |