CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Cross-Domain Collaborative Learning in Social Multimedia
Shengsheng Qian1; Tianzhu Zhang1; Richang Hong2; Changsheng Xu1
2015-10
Conference NameACM international conference on Multimedia
Conference DateOctober 26 - 30, 2015
Conference PlaceBrisbane, Australia
Abstract

Cross-domain data analysis is one of the most important tasks in social multimedia. It has a wide range of real-world applications, including cross-platform event analysis, cross-domain multi-event tracking, cross-domain video recommendation, etc. It is also very challenging because the data have multi-modal and multi-domain properties, and there are no explicit correlations to link different domains. To deal with these issues, we propose a generic Cross-Domain Collaborative Learning (CDCL) framework based on non-parametric Bayesian dictionary learning model for cross-domain data analysis. In the proposed CDCL model, it can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property. As a result, our CDCL model can effectively explore the virtues of different information sources to complement and enhance each other for cross-domain data analysis. To evaluate the proposed model, we apply it for two different applications: cross-platform event recognition and cross-network video recommendation. The extensive experimental evaluations well demonstrate the effectiveness of the proposed algorithm for cross-domain data analysis.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14481
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorChangsheng Xu
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.School of Computer and Information, Hefei University of Technology
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shengsheng Qian,Tianzhu Zhang,Richang Hong,et al. Cross-Domain Collaborative Learning in Social Multimedia[C],2015.
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