Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation | |
Jian Liang1,2; Ran He1,2,3; Zhenan Sun1,2,3; Tieniu Tan1,2,3 | |
发表期刊 | IEEE Trans. Pattern Anal. Machine Intell. |
2019 | |
卷号 | 41期号:5页码:1027-1042 |
文章类型 | regular paper |
摘要 | Unsupervised domain adaptation aims to leverage the labeled source data to learn with the unlabeled target data. Previous trandusctive methods tackle it by iteratively seeking a low-dimensional projection to extract the invariant features and obtaining the pseudo target labels via building a classifier on source data. However, they merely concentrate on minimizing the cross-domain distribution divergence, while ignoring the intra-domain structure especially for the target domain. Even after projection, possible risk factors like imbalanced data distribution may still hinder the performance of target label inference. In this paper, we propose a simple yet effective domain-invariant projection ensemble approach to tackle these two issues together. Specifically, we seek the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains. To further enhance the target label inference, we first develop a `sampling-and-fusion' framework, under which multiple projections are independently learned based on various randomized coupled domain subsets. Subsequently, aggregating models such as majority voting are utilized to leverage multiple projections and classify unlabeled target data. |
关键词 | Unsupervised Domain Adaptation Domain-invariant Projection Class-clustering Sampling-and-fusion |
收录类别 | SCI ; SCIE ; SSCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000463607400001 |
七大方向——子方向分类 | 模式识别基础 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23803 |
专题 | 模式识别实验室 |
通讯作者 | Zhenan Sun; Tieniu Tan |
作者单位 | 1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Jian Liang,Ran He,Zhenan Sun,et al. Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation[J]. IEEE Trans. Pattern Anal. Machine Intell.,2019,41(5):1027-1042. |
APA | Jian Liang,Ran He,Zhenan Sun,&Tieniu Tan.(2019).Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation.IEEE Trans. Pattern Anal. Machine Intell.,41(5),1027-1042. |
MLA | Jian Liang,et al."Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation".IEEE Trans. Pattern Anal. Machine Intell. 41.5(2019):1027-1042. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
final.pdf(865KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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