Boosted Multifeature Learning for Cross-Domain Transfer
文献类型：期刊论文
| 作者 | Yang, Xiaoshan1,2 ; Zhang, Tianzhu1,2; Xu, Changsheng1,2; Yang, Ming-Hsuan3; Xu CS(徐常胜)
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| 刊名 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
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| 出版日期 | 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 |
| 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 |
| 通讯作者 | Xu CS(徐常胜) |
| 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,&徐常胜.(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). |
入库方式： OAI收割
来源：自动化研究所
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