Distribution-Free One-Pass Learning
Zhao, Peng1; Wang, Xinqiang2; Xie, Siyu3; Guo, Lei2; Zhou, Zhi-Hua1
AbstractIn 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.
KeywordData models Random variables Proposals Training Prediction algorithms Task analysis Compressed sensing Distribution change one-pass learning robust learning non-stationary environments
Indexed BySCI
Funding ProjectNational Key R&D Program of China[2018YFB1004300] ; National Science Foundation of China[61921006] ; Collaborative Innovation Center of Novel Software Technology and Industrialization
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000615042700010
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Document Type期刊论文
Corresponding AuthorZhou, Zhi-Hua
Affiliation1.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
Recommended Citation
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.
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