Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99372
Title: Domain transfer multiple kernel learning
Authors: Duan, Lixin
Tsang, Ivor Wai-Hung
Xu, Dong
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Duan, L., Tsang, I. W., & Xu, D. (2012). Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 465-479.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.
URI: https://hdl.handle.net/10356/99372
http://hdl.handle.net/10220/13492
ISSN: 0162-8828
DOI: http://dx.doi.org/10.1109/TPAMI.2011.114
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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