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  5. 多媒体计算与图形学团队
Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model

文献类型：期刊论文

作者Qian, Shengsheng1,2; Zhang, Tianzhu1,2; Xu, Changsheng1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2018-08-01
卷号20期号:8页码:2086-2099
关键词Social media discriminative non-parametric Bayesian model multi-modality
ISSN号1520-9210
DOI10.1109/TMM.2017.2785227
通讯作者Qian, Shengsheng(sheng.qian@nlpr.ia.ac.cn)
英文摘要Cross-domain data analysis has been becoming more and more important, and can he effectively adopted for many applications. However, it is difficult to propose a unified cross-domain collaborative learning framework for cross-domain analysis in social multimedia, because cross-domain data have multidomain, multimodal, sparse, and supervised properties. In this paper, we propose a generic cross-domain collaborative learning (CDCL) framework via a discriminative non-parametric Bayesian dictionary learning model for cross-domain data analysis. Compared with existing cross-domain learning methods, our proposed model mainly has four advantages: First, to address the domain discrepancy, we utilize the shared domain priors among multiple domains to make them share a common feature space. Second, to exploit the multimodal property, we use the shared modality priors to model the relationship between different modalities. Third, to deal with the sparse property of media data in one domain, our goal is to learn a shared dictionary to bridge different domains and complement each other. Finally, to make use of the supervised property, we exploit class label information to learn the shared discriminative dictionary, and utilize a latent probability vector to select different dictionary elements for representation of each class. Therefore, the proposed model can investigate the superiorities of different sources to supplement and improve each other effectively. In experiments, we have evaluated our model for two important applications including cross-platform event recognition and cross-network video recommendation. The experimental results have showed the effectiveness of our CDCL model for cross-domain analysis.
WOS关键词SPARSE REPRESENTATION ; IMAGE SUPERRESOLUTION ; K-SVD ; RECOGNITION ; DICTIONARY ; CLASSIFICATION ; MULTIMEDIA ; RETRIEVAL ; SEARCH ; WEB
资助项目National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61572296] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; Beijing Natural Science Foundation[4172062]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000439378600014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/26314]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Qian, Shengsheng
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Qian, Shengsheng,Zhang, Tianzhu,Xu, Changsheng. Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(8):2086-2099.
APA Qian, Shengsheng,Zhang, Tianzhu,&Xu, Changsheng.(2018).Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model.IEEE TRANSACTIONS ON MULTIMEDIA,20(8),2086-2099.
MLA Qian, Shengsheng,et al."Cross-Domain Collaborative Learning via Discriminative Nonparametric Bayesian Model".IEEE TRANSACTIONS ON MULTIMEDIA 20.8(2018):2086-2099.

入库方式： OAI收割

来源：自动化研究所

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