Distribution-Free One-Pass Learning

doi:10.1109/TKDE.2019.2937078

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	Distribution-Free One-Pass Learning
	Zhao, Peng 1; Wang, Xinqiang 2; Xie, Siyu 3; Guo, Lei 2; Zhou, Zhi-Hua 1
	2021-03-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	33 期号:3 页码:951-963
摘要	In many large-scale machine learning applications, data are accumulated over time, and thus, an appropriate model should be able to update in an online style. In particular, it would be ideal to have a storage independent from the data volume, and scan each data item only once. Meanwhile, the data distribution usually changes during the accumulation procedure, making distribution-free one-pass learning a challenging task. In this paper, we propose a simple yet effective approach for this task, without requiring prior knowledge about the change, where every data item can be discarded once scanned. We also present a variant for high-dimensional situations, by exploiting compressed sensing to reduce computational and storage complexity. Theoretical analysis shows that our proposal converges under mild assumptions, and the performance is validated on both synthetic and real-world datasets.
关键词	Data models Random variables Proposals Training Prediction algorithms Task analysis Compressed sensing Distribution change one-pass learning robust learning non-stationary environments
DOI	10.1109/TKDE.2019.2937078
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018YFB1004300] ; National Science Foundation of China[61921006] ; Collaborative Innovation Center of Novel Software Technology and Industrialization
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000615042700010
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58198
专题	中国科学院数学与系统科学研究院
通讯作者	Zhou, Zhi-Hua
作者单位	1.Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 3.Wayne State Univ, Dept Elect & Comp Engn, Detroit, MI 48202 USA
推荐引用方式 GB/T 7714	Zhao, Peng,Wang, Xinqiang,Xie, Siyu,et al. Distribution-Free One-Pass Learning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(3):951-963.
APA	Zhao, Peng,Wang, Xinqiang,Xie, Siyu,Guo, Lei,&Zhou, Zhi-Hua.(2021).Distribution-Free One-Pass Learning.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(3),951-963.
MLA	Zhao, Peng,et al."Distribution-Free One-Pass Learning".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.3(2021):951-963.