CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Unified Cross-domain Classification via Geometric and Statistical Adaptations
Liu, Weifeng1; Li, Jinfeng2; Liu, Baodi1; Guan, Weili3; Zhou, Yicong4; Xu, Changsheng5
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2021-02-01
Volume110Pages:9
Corresponding AuthorLiu, Weifeng(liuwf@upc.edu.cn)
AbstractDomain adaptation aims to learn an adaptive classifier for target data using the labelled source data from a different distribution. Most proposed works construct cross-domain classifier by exploring one-sided property of the input data, i.e., either geometric or statistical property. Therefore they may ignore the complementarity between the two properties. Moreover, many previous methods implement knowledge transfer with two separated steps: divergence minimization and classifier construction, which degrades the adaptation robustness. In order to address such problems, we propose a unified cross-domain classi-fication method via geometric and statistical adaptations (UCGS). UCGS models the divergence minimization and classifier construction in a unified way based on structural risk minimization principle and coupled adaptations theory. Specifically, UCGS constructs an adaptive model by simultaneously minimizing the structural risk on labelled source data, using Maximum Mean Discrepancy (MMD) criterion to implement statistical adaptation, and flexibly employing the Nystrom method to explore the geometric connections between domains. A domain-invariant graph is successfully constructed to link the two domains geometrically. The standard supervised methods can be used to instantiate UCGS to handle inter-domain classification problems. Comprehensive experiments show the superiority of UCGS on several real-world datasets. (c) 2020 Elsevier Ltd. All rights reserved.
KeywordDomain adaptation Statistical adaptation Maximum mean discrepancy (MMD) Geometric adaptation Nystrom method
DOI10.1016/j.patcog.2020.107658
WOS KeywordREGULARIZATION ; FRAMEWORK ; KERNEL
Indexed BySCI
Language英语
Funding ProjectMajor Scientific and Technological Projects of CNPC[ZD2019-183-008] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[2020 0 0009] ; Fundamental Research Funds for the Central Universities[20CX05004A]
Funding OrganizationMajor Scientific and Technological Projects of CNPC ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Fundamental Research Funds for the Central Universities
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000585303400011
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41650
Collection模式识别国家重点实验室_多媒体计算
Corresponding AuthorLiu, Weifeng
Affiliation1.China Univ Petr East China, Coll Control Sci & Engn, Beijing, Peoples R China
2.China Univ Petr East China, Coll Oceanog & Space Informat, Beijing, Peoples R China
3.Monash Univ, Fac Informat Technol, Clayton Campus, Clayton, Vic, Australia
4.Univ Macau, Fac Sci & Technol, Macau, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Weifeng,Li, Jinfeng,Liu, Baodi,et al. Unified Cross-domain Classification via Geometric and Statistical Adaptations[J]. PATTERN RECOGNITION,2021,110:9.
APA Liu, Weifeng,Li, Jinfeng,Liu, Baodi,Guan, Weili,Zhou, Yicong,&Xu, Changsheng.(2021).Unified Cross-domain Classification via Geometric and Statistical Adaptations.PATTERN RECOGNITION,110,9.
MLA Liu, Weifeng,et al."Unified Cross-domain Classification via Geometric and Statistical Adaptations".PATTERN RECOGNITION 110(2021):9.
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