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

doi:10.1016/j.neunet.2019.12.002

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Discriminative structure learning of sum-product networks for data stream classification
	Sun, Zhengya1 ; Liu, Cheng-Lin2,3,4 ; Niu, Jinghao1 ; Zhang, Wensheng1,3
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2020-03-01
卷号	123 页码:163-175
通讯作者	Sun, Zhengya(zhengya.sun@ia.ac.cn)
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.
关键词	Sum-product network Discriminative structure learning Data stream classification
DOI	10.1016/j.neunet.2019.12.002
收录类别	SCI
语种	英语
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] ; 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]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Beijing Municipality
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000511985000014
出版者	PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类	机器学习
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28625
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Sun, Zhengya
作者单位	1.Chinese Acad Sci, Precise Percept & Control Res Ctr, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China 3.UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, 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.