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Locality-Based Discriminant Feature Selection with Trace Ratio
Guo, Muhan1; Yang, Sheng1; Nie, Feiping1; Li, Xuelong2
2018-08-29
Conference Name25th IEEE International Conference on Image Processing, ICIP 2018
Source Publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Pages3373-3377
Conference Date2018-10-07
Conference PlaceAthens, Greece
PublisherIEEE Computer Society
Contribution Rank2
AbstractFeature 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.
Department光学影像学习与分析中心
DOI10.1109/ICIP.2018.8451109
Indexed ByEI
ISBN9781479970612
Language英语
ISSN15224880
EI Accession Number20191206646380
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31346
Collection光学影像学习与分析中心
Affiliation1.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
Recommended Citation
GB/T 7714
Guo, Muhan,Yang, Sheng,Nie, Feiping,et al. Locality-Based Discriminant Feature Selection with Trace Ratio[C]:IEEE Computer Society,2018:3373-3377.
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