Self-Weighted Adaptive Locality Discriminant Analysis | |
Guo, Muhan1; Nie, Feiping1; Li, Xuelong2![]() | |
2018-08-29 | |
会议日期 | 2018-10-07 |
会议地点 | Athens, Greece |
DOI | 10.1109/ICIP.2018.8451023 |
页码 | 3378-3382 |
英文摘要 | The linear discriminant analysis (LDA) is a popular technique for dimensionality reduction, nevertheless, when the input data lie in a complicated geometry distribution, LDA tends to obtain undesired results since it neglects the local structure of data. Though plenty of previous works devote to capturing the local structure, they have the same weakness that the neighbors found in the original data space may be not reliable, especially when noise is large. In this paper, we propose a novel supervised dimensionality reduction approach, Self-weighted Adaptive Locality Discriminant Analysis (SALDA), which aims to find a representative low-dimensional subspace of data. Compared with LDA and its variants, SALDA explores the neighborhood relationship of data points in the desired subspace effectively. Besides, the weights between within-class data points are learned automatically without setting any additional parameter. Extensive experiments on synthetic and real-world datasets show 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
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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/181661/31344] ![]() |
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.Center for OPTical IMagery Analysis and Learning (OPTIMAL), 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,Nie, Feiping,Li, Xuelong. Self-Weighted Adaptive Locality Discriminant Analysis[C]. 见:. Athens, Greece. 2018-10-07. |
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