Boosted Multifeature Learning for Cross-Domain Transfer

doi:10.1145/2700286

	Boosted Multifeature Learning for Cross-Domain Transfer
	Yang, Xiaoshan1,2 ; Zhang, Tianzhu1,2 ; Xu, Changsheng1,2 ; Yang, Ming-Hsuan 3; Xu CS(徐常胜)
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2015
卷号	11 期号:3 页码:35:1-18
文章类型	Article
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.
关键词	Algorithms Experimentation Performance Domain Adaptation Multifeature Boosting Denoising Auto-encoder
WOS标题词	Science & Technology ; Technology
DOI	10.1145/2700286
关键词[WOS]	ADAPTATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000349852500003
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8044
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu CS(徐常胜)
作者单位	1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.China Singapore Inst Digital Media, Singapore 119613, Singapore 3.Univ Calif, Dept Elect Engn & Comp Sci, Merced, CA 95334 USA
第一作者单位	中国科学院自动化研究所
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):35:1-18.
APA	Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,Yang, Ming-Hsuan,&徐常胜.(2015).Boosted Multifeature Learning for Cross-Domain Transfer.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,11(3),35:1-18.
MLA	Yang, Xiaoshan,et al."Boosted Multifeature Learning for Cross-Domain Transfer".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 11.3(2015):35:1-18.

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