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Title:
Cross-domain metric learning based on information theory
Author: Wang, Hao (1) ; Wang, Wei (2) ; Zhang, Chen (2) ; Xu, Fanjiang (2)
Conference Name: 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Conference Date: July 27, 2014 - July 31, 2014
Issued Date: 2014
Conference Place: Quebec City, QC, Canada
Corresponding Author: Wang, Hao
Publish Place: AI Access Foundation
Indexed Type: EI
ISBN: 9781577356790
Department: (1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Science and Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China; (3) Department of Automation, University of Science and Technology, China
Abstract: Supervised metric learning plays a substantial role in statistical classification. Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains (i.e., source domain and target domain). Although this issue has got some progress in feature-based transfer learning, most of the work in this area suffers from non-trivial optimization and pays little attention to preserving the discriminating information. In this paper, we propose a novel metric learning algorithm to transfer knowledge from the source domain to the target domain in an information-theoretic setting, where a shared Mahalanobis distance across two domains is learnt by combining three goals together: 1) reducing the distribution difference between different domains; 2) preserving the geometry of target domain data; 3) aligning the geometry of source domain data with its label information. Based on this combination, the learnt Mahalanobis distance effectively transfers the discriminating power and propagates standard classifiers across these two domains. More importantly, our proposed method has closed-form solution and can be efficiently optimized. Experiments in two real-world applications demonstrate the effectiveness of our proposed method.
English Abstract: Supervised metric learning plays a substantial role in statistical classification. Conventional metric learning algorithms have limited utility when the training data and testing data are drawn from related but different domains (i.e., source domain and target domain). Although this issue has got some progress in feature-based transfer learning, most of the work in this area suffers from non-trivial optimization and pays little attention to preserving the discriminating information. In this paper, we propose a novel metric learning algorithm to transfer knowledge from the source domain to the target domain in an information-theoretic setting, where a shared Mahalanobis distance across two domains is learnt by combining three goals together: 1) reducing the distribution difference between different domains; 2) preserving the geometry of target domain data; 3) aligning the geometry of source domain data with its label information. Based on this combination, the learnt Mahalanobis distance effectively transfers the discriminating power and propagates standard classifiers across these two domains. More importantly, our proposed method has closed-form solution and can be efficiently optimized. Experiments in two real-world applications demonstrate the effectiveness of our proposed method.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16594
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Wang, Hao ,Wang, Wei ,Zhang, Chen ,et al. Cross-domain metric learning based on information theory[C]. 见:28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014. Quebec City, QC, Canada. July 27, 2014 - July 31, 2014.
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