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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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 26; H. 5; S. 1076 - 1089
Hauptverfasser: Mingsheng Long, Jianmin Wang, Guiguang Ding, Pan, Sinno Jialin, Yu, Philip S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.111