Adaptation Regularization: A General Framework for Transfer Learning

Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: di...

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Bibliographic Details
Published in:	IEEE transactions on knowledge and data engineering Vol. 26; no. 5; pp. 1076 - 1089
Main Authors:	Mingsheng Long, Jianmin Wang, Guiguang Ding, Pan, Sinno Jialin, Yu, Philip S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial Intelligence Classifier design and evaluation Computing Methodologies Database Applications Database Management Design Methodology Feature extraction Information Technology and Systems Joints Kernel Knowledge acquisition Learning Manifolds Mining methods and algorithms Modeling structured Pattern Recognition Probability distribution Risk management textual and multimedia data manifold regularization distribution adaptation Transfer learning generalization error adaptation regularization
ISSN:	1041-4347, 1558-2191
Online Access:	Get full text
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Abstract	Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets.
AbstractList	Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets. Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets. [PUBLICATION ABSTRACT]
Author	Guiguang Ding Pan, Sinno Jialin Yu, Philip S. Mingsheng Long Jianmin Wang
Author_xml	– sequence: 1 surname: Mingsheng Long fullname: Mingsheng Long email: longming-sheng@gmail.com organization: Sch. of Software, Tsinghua Univ., Beijing, China – sequence: 2 surname: Jianmin Wang fullname: Jianmin Wang email: jimwang@tsinghua.edu.cn organization: Sch. of Software, Tsinghua Univ., Beijing, China – sequence: 3 surname: Guiguang Ding fullname: Guiguang Ding email: dinggg@tsinghua.edu.cn organization: Sch. of Software, Tsinghua Univ., Beijing, China – sequence: 4 givenname: Sinno Jialin surname: Pan fullname: Pan, Sinno Jialin email: jspan@i2r.a-star.edu.sg organization: Inst. of Infocomm Res., Singapore, Singapore – sequence: 5 givenname: Philip S. surname: Yu fullname: Yu, Philip S. email: psyu@uic.edu organization: Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
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SubjectTerms	Artificial Intelligence Classifier design and evaluation Computing Methodologies Database Applications Database Management Design Methodology Feature extraction Information Technology and Systems Joints Kernel Knowledge acquisition Learning Manifolds Mining methods and algorithms Modeling structured Pattern Recognition Probability distribution Risk management textual and multimedia data
Title	Adaptation Regularization: A General Framework for Transfer Learning
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