Unsupervised feature selection with structured graph optimization | |
Nie, Feiping1; Zhu, Wei1; Li, Xuelong2 | |
2016 | |
会议名称 | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
会议录名称 | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
页码 | 1302-1308 |
会议日期 | 2016-02-12 |
会议地点 | Phoenix, AZ, United states |
出版者 | AAAI press |
产权排序 | 2 |
摘要 | Since amounts of unlabelled and high-dimensional data needed to be processed, unsupervised feature selection has become an important and challenging problem in machine learning. Conventional embedded unsupervised methods always need to construct the similarity matrix, which makes the selected features highly depend on the learned structure. However real world data always contain lots of noise samples and features that make the similarity matrix obtained by original data can't be fully relied. We propose an unsupervised feature selection approach which performs feature selection and local structure learning simultaneously, the similarity matrix thus can be determined adaptively. Moreover, we constrain the similarity matrix to make it contain more accurate information of data structure, thus the proposed approach can select more valuable features. An efficient and simple algorithm is derived to optimize the problem. Experiments on various benchmark data sets, including handwritten digit data, face image data and biomedical data, validate the effectiveness of the proposed approach. © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
关键词 | Artificial Intelligence Clustering Algorithms Human Computer Interaction Learning Systems Matrix Algebra |
学科领域 | Artificial Intelligence |
作者部门 | 光学影像学习与分析中心 |
收录类别 | EI |
ISBN号 | 9781577357605 |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28585 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China 2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), 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 | Nie, Feiping,Zhu, Wei,Li, Xuelong. Unsupervised feature selection with structured graph optimization[C]:AAAI press,2016:1302-1308. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Unsupervised feature(699KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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