Online learning from capricious data streams: A generative approach
文献类型：会议论文
| 作者 | He, Yi1; Wu, Baijun1; Wu, Di2 ; Beyazit, Ege1; Chen, Sheng1; Wu, Xindong1
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| 出版日期 | 2019 |
| 会议日期 | August 10, 2019 - August 16, 2019 |
| 会议地点 | Macao, China |
| 页码 | 2491-2497 |
| 英文摘要 | Learning with streaming data has received extensive attention during the past few years. Existing approaches assume the feature space is fixed or changes by following explicit regularities, limiting their applicability in dynamic environments where the data streams are described by an arbitrarily varying feature space. To handle such capricious data streams, we in this paper develop a novel algorithm, named OCDS (Online learning from Capricious Data Streams), which does not make any assumption on feature space dynamics. OCDS trains a learner on a universal feature space that establishes relationships between old and new features, so that the patterns learned in the old feature space can be used in the new feature space. Specifically, the universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve the performance with theoretical analysis. The experimental results demonstrate that OCDS achieves conspicuous performance on both synthetic and real datasets. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. |
| 会议录 | 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
 |
| 语种 | 英语 |
| ISSN号 | 10450823 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9795]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 作者单位 | 1.School of Computing and Informatics, University of Louisiana, Lafayette, United States; 2.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | He, Yi,Wu, Baijun,Wu, Di,et al. Online learning from capricious data streams: A generative approach[C]. 见:. Macao, China. August 10, 2019 - August 16, 2019. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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