Details

Autor(en) / Beteiligte

• Lixin Duan
• Tsang, I. W.
• Dong Xu

Titel

Domain Transfer Multiple Kernel Learning

Ist Teil von

• IEEE transactions on pattern analysis and machine intelligence, 2012-03, Vol.34 (3), p.465-479

Ort / Verlag

Los Alamitos, CA: IEEE

Erscheinungsjahr

2012

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.