Incomplete Multi-View Weak-Label Learning
Authors: Qiaoyu Tan, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world datasets validate the effectiveness of our model against other competitive algorithms. Experiments on five widely used datasets and comparisons with a number of competitive methods [Yuan et al., 2012; Zhang et al., 2013; Xu et al., 2015a; Liu et al., 2015] demonstrate the superiority of the proposed work. |
| Researcher Affiliation | Academia | 1College of Computer and Information Science, Southwest University, Chongqing 400715, China 2Department of Computer Science, George Mason University, Fairfax 22030, USA 3School of Information Technology, Deakin University, Geelong, VIC 3220, Australia |
| Pseudocode | No | The paper describes the optimization steps in textual format (I, II, III, IV) with equations, but does not provide a formally labeled "Algorithm" or "Pseudocode" block. |
| Open Source Code | Yes | The source code of i MVWL is publicly available at http://mlda.swu.edu.cn/codes.php?name=i MVWL. |
| Open Datasets | Yes | The five multi-view datasets used in the experiments (Core15k, Pascal07, ESPGame, IAPRTC-12, and Mirflicker) are summarized in Table 1. These datasets1 are obtained from [Guillaumin et al., 2010], and each is represented by six feature views: HUE, SIFT, GIST, HSV, RGB, and LAB. 1Available at http://lear.inrialpes.fr/people/guillaumin/data.php |
| Dataset Splits | Yes | For each dataset, we randomly sample 70% of the data for training, and use the remaining 30% data for testing (unlabeled data). Fivefold cross validation on the training set is used to select the optimal parameter values for each competitive method. |
| Hardware Specification | Yes | The experiments are conducted on Cent OS 6.9 with Inter(R) Xeon E5-2678, 64GB RAM and MATLAB 2013a. |
| Software Dependencies | Yes | The experiments are conducted on Cent OS 6.9 with Inter(R) Xeon E5-2678, 64GB RAM and MATLAB 2013a. |
| Experiment Setup | Yes | For our method, we selected the parameters α and β from {10i|i = 5, , 0}. Experimental results show that i MVWL yields relatively stable performance with α around 10 2 and β around 10 2, and therefore we use these values. |