Domain adaptation from multiple sources via auxiliary classifiers

Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target domain by leveraging a set of pre-computed classifiers (referred to as auxiliary/source classifiers) independently learned with the labeled patterns from multiple source domains. We introduce a new datadependent regularizer based on smoothness assumption into Least-Squares
more » ... (LS-SVM), which enforces that the target classifier shares similar decision values with the auxiliary classifiers from relevant source domains on the unlabeled patterns of the target domain. In addition, we employ a sparsity regularizer to learn a sparse target classifier. Comprehensive experiments on the challenging TRECVID 2005 corpus demonstrate that DAM outperforms the existing multiple source domain adaptation methods for video concept detection in terms of effectiveness and efficiency.
doi:10.1145/1553374.1553411 dblp:conf/icml/DuanTXC09 fatcat:nrtgo4ekkvdaboylflbhvrwwcu