Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification | |
Nie, Feiping1; Li, Jing1; Li, Xuelong2 | |
2016 | |
会议名称 | 25th international joint conference on artificial intelligence, ijcai 2016 |
会议日期 | 2016-07-09 |
会议地点 | new york, ny, united states |
关键词 | Artificial intelligence Graphic methods Learning algorithms Virtual reality |
卷号 | 2016-january |
页码 | 1881-1887 |
英文摘要 | graph-based approaches have been successful in unsupervised and semi-supervised learning. in this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. the key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. in this paper, we propose a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semisupervised tasks. unlike other methods in the literature, the proposed methods can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. furthermore, our objective under semisupervised learning is convex and the global optimal result will be obtained. extensive empirical results on different real-world data sets demonstrate that the proposed methods achieve comparable performance with the state-of-the-art approaches and can be used more practically. |
收录类别 | EI |
产权排序 | 2 |
会议录 | proceedings of the 25th international joint conference on artificial intelligence, ijcai 2016 |
会议录出版者 | international joint conferences on artificial intelligence |
学科主题 | computer software, data handling and applications ; artificial intelligence |
语种 | 英语 |
ISSN号 | 10450823 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/28575] |
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, 710072, China 2.Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China |
推荐引用方式 GB/T 7714 | Nie, Feiping,Li, Jing,Li, Xuelong. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification[C]. 见:25th international joint conference on artificial intelligence, ijcai 2016. new york, ny, united states. 2016-07-09. |
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