Locality-Based Discriminant Feature Selection with Trace Ratio | |
Guo, Muhan1; Yang, Sheng1; Nie, Feiping1; Li, Xuelong2 | |
2018-08-29 | |
会议日期 | 2018-10-07 |
会议地点 | Athens, Greece |
DOI | 10.1109/ICIP.2018.8451109 |
页码 | 3373-3377 |
英文摘要 | Feature selection plays an important role to select the informative and valuable features especially in high-dimensional data. However, some conventional feature selection methods select the features according to a feature subset score, which are often time-consuming, not quite robust to noise and neglecting the local data structure. To address this problem, we propose a novel feature selection approach, namely locality-based discriminant feature selection with trace ratio (LDFS), which can perform local data structure learning, and feature selection simultaneously. Furthermore, the proposed approach is robust to data noise and can pick out genuinely valuable features. In the end, experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method. © 2018 IEEE. |
产权排序 | 2 |
会议录 | 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings |
会议录出版者 | IEEE Computer Society |
语种 | 英语 |
ISSN号 | 15224880 |
ISBN号 | 9781479970612 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/31346] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China; 2.State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China |
推荐引用方式 GB/T 7714 | Guo, Muhan,Yang, Sheng,Nie, Feiping,et al. Locality-Based Discriminant Feature Selection with Trace Ratio[C]. 见:. Athens, Greece. 2018-10-07. |
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