Online Bayesian max-margin subspace learning for multi-view classification and regression

doi:10.1007/s10994-019-05853-8

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Online Bayesian max-margin subspace learning for multi-view classification and regression
	He, Jia 1,2,4; Du, Changying 3,5; Zhuang, Fuzhen 1,4; Yin, Xin 1; He, Qing 1,4; Long, Guoping 5
刊名	MACHINE LEARNING
	2019-10-25
页码	31
关键词	Multi-view learning Online learning Bayesian subspace learning Max-margin Classification Regression
ISSN号	0885-6125
DOI	10.1007/s10994-019-05853-8
英文摘要	Multi-view data have become increasingly popular in many real-world applications where data are generated from different information channels or different views such as image + text, audio + video, and webpage + link data. Last decades have witnessed a number of studies devoted to multi-view learning algorithms, especially the predictive latent subspace learning approaches which aim at obtaining a subspace shared by multiple views and then learning models in the shared subspace. However, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm which learns predictive subspace with the max-margin principle. Specifically, we first define the latent margin loss for classification or regression in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian passive-aggressive style. Finally, we extensively evaluate our model on several real-world data sets and the experimental results show that our models can achieve superior performance, compared with a number of state-of-the-art competitors.
资助项目	National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61602449] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[61773361] ; Project of Youth Innovation Promotion Association CAS[2017146]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000492576000002
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14920]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Du, Changying
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Huawei EI Innovat Lab, Beijing 100085, Peoples R China 3.Huawei Noahs Ark Lab, Beijing 100085, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Software, Lab Parallel Software & Computat Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	He, Jia,Du, Changying,Zhuang, Fuzhen,et al. Online Bayesian max-margin subspace learning for multi-view classification and regression[J]. MACHINE LEARNING,2019:31.
APA	He, Jia,Du, Changying,Zhuang, Fuzhen,Yin, Xin,He, Qing,&Long, Guoping.(2019).Online Bayesian max-margin subspace learning for multi-view classification and regression.MACHINE LEARNING,31.
MLA	He, Jia,et al."Online Bayesian max-margin subspace learning for multi-view classification and regression".MACHINE LEARNING (2019):31.