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Discriminative structure learning of sum-product networks for data stream classification

文献类型：期刊论文

作者Sun, Zhengya3; Liu, Cheng-Lin1,2,4; Niu, Jinghao3; Zhang, Wensheng1,3
刊名NEURAL NETWORKS
出版日期2020-03-01
卷号123页码:163-175
关键词Sum-product network Discriminative structure learning Data stream classification
ISSN号0893-6080
DOI10.1016/j.neunet.2019.12.002
通讯作者Sun, Zhengya(zhengya.sun@ia.ac.cn)
英文摘要Sum-product network (SPN) is a deep probabilistic representation that allows for exact and tractable inference. There has been a trend of online SPN structure learning from massive and continuous data streams. However, online structure learning of SPNs has been introduced only for the generative settings so far. In this paper, we present an online discriminative approach for SPNs for learning both the structure and parameters. The basic idea is to keep track of informative and representative examples to capture the trend of time-changing class distributions. Specifically, by estimating the goodness of model fitting of data points and dynamically maintaining a certain amount of informative examples over time, we generate new sub-SPNs in a recursive and top-down manner. Meanwhile, an outlier-robust margin-based log-likelihood loss is applied locally to each data point and the parameters of SPN are updated continuously using most probable explanation (MPE) inference. This leads to a fast yet powerful optimization procedure and improved discrimination capability between the genuine class and rival classes. Empirical results show that the proposed approach achieves better prediction performance than the state-of-the-art online structure learner for SPNs, while promising order-ofmagnitude speedup. Comparison with state-of-the-art stream classifiers further proves the superiority of our approach. (C) 2019 Elsevier Ltd. All rights reserved.
资助项目National Key R&D Program of China[2017YFC0803700] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[U1636220] ; Natural Science Foundation of Beijing Municipality[4172063]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000511985000014
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Beijing Municipality
源URL[http://ir.ia.ac.cn/handle/173211/28624]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Sun, Zhengya
作者单位1.UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Precise Percept & Control Res Ctr, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Zhengya,Liu, Cheng-Lin,Niu, Jinghao,et al. Discriminative structure learning of sum-product networks for data stream classification[J]. NEURAL NETWORKS,2020,123:163-175.
APA Sun, Zhengya,Liu, Cheng-Lin,Niu, Jinghao,&Zhang, Wensheng.(2020).Discriminative structure learning of sum-product networks for data stream classification.NEURAL NETWORKS,123,163-175.
MLA Sun, Zhengya,et al."Discriminative structure learning of sum-product networks for data stream classification".NEURAL NETWORKS 123(2020):163-175.

入库方式： OAI收割

来源：自动化研究所

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