CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Semi-supervised domain adaptation via Fredholm integral based kernel methods
Wang, Wei1; Wang, Hao2; Zhang, Zhaoxiang3; Zhang, Chen2; Gao, Yang1
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019
Volume85Pages:185-197
Corresponding AuthorWang, Wei(wangwei2014@iscas.ac.cn)
AbstractAlong with the emergence of domain adaptation in semi-supervised setting, dealing with the noisy and complex data in classifier adaptation underscores its growing importance. We believe a large amount of unlabeled data in target domain, which are always only used in distribution alignment, are more of a great source of information for this challenge. In this paper, we propose a novel Transfer Fredholm Multiple Kernel Learning (TFMKL) framework to suppress the noise for complex data distributions. Firstly, with exploring unlabeled target data, TFMKL learns a cross-domain predictive model by developing a Fredholm integral based kernel prediction framework which is proven to be effective in noise suppression. Secondly, TFMKL explicitly extends the applied range of unlabeled target samples into adaptive classifier building and distribution alignment. Thirdly, multiple kernels are explored to induce an optimal learning space. Correspondingly, TFMKL is distinguished with allowing for noise resiliency, facilitating knowledge transfer and analyzing complex data characteristics at the same time. Furthermore, an effective optimization procedure is presented based on the reduced gradient, guaranteeing rapid convergence. We emphasize the adaptability of TFMKL to different domain adaptation tasks due to its extension to different predictive models. In particular, two models based on square loss and hinge loss respectively are proposed within the TFMKL framework. Comprehensive empirical studies on benchmark data sets verify the effectiveness and the noise resiliency of our proposed methods. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordDomain adaptation Semi-supervised learning Multiple kernel learning Hilbert space embedding of distributions
DOI10.1016/j.patcog.2018.07.035
WOS KeywordREGULARIZATION ; REPRESENTATION ; CLASSIFIERS ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[61502466] ; Natural Science Foundation of China[61773375] ; Natural Science Foundation of China[61672501] ; Natural Science Foundation of China[61602453] ; Beijing Municipal Natural Science Foundation[Z181100008918010]
Funding OrganizationNatural Science Foundation of China ; Beijing Municipal Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000447819300016
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22813
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorWang, Wei
Affiliation1.Chinese Acad Sci, Inst Software, Beijing, Peoples R China
2.360 Search Lab, Beijing 360, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wang, Wei,Wang, Hao,Zhang, Zhaoxiang,et al. Semi-supervised domain adaptation via Fredholm integral based kernel methods[J]. PATTERN RECOGNITION,2019,85:185-197.
APA Wang, Wei,Wang, Hao,Zhang, Zhaoxiang,Zhang, Chen,&Gao, Yang.(2019).Semi-supervised domain adaptation via Fredholm integral based kernel methods.PATTERN RECOGNITION,85,185-197.
MLA Wang, Wei,et al."Semi-supervised domain adaptation via Fredholm integral based kernel methods".PATTERN RECOGNITION 85(2019):185-197.
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