Boosted Multifeature Learning for Cross-Domain Transfer

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Boosted Multifeature Learning for Cross-Domain Transfer
	Yang, Xiaoshan1,2 ; Zhang, Tianzhu1,2 ; Xu, Changsheng1,2 ; Yang, Ming-Hsuan 3
刊名	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2015
卷号	11 期号:3
关键词	Algorithms Experimentation Performance Domain adaptation multifeature boosting denoising auto-encoder
英文摘要	Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
研究领域[WOS]	Computer Science
关键词[WOS]	ADAPTATION
收录类别	SCI
语种	英语
WOS记录号	WOS:000349852500003
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/8044]
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
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119613, Singapore 3.Univ Calif, Dept Elect Engn & Comp Sci, Merced, CA 95334 USA
推荐引用方式 GB/T 7714	Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Boosted Multifeature Learning for Cross-Domain Transfer[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2015,11(3).
APA	Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,&Yang, Ming-Hsuan.(2015).Boosted Multifeature Learning for Cross-Domain Transfer.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,11(3).
MLA	Yang, Xiaoshan,et al."Boosted Multifeature Learning for Cross-Domain Transfer".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 11.3(2015).